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Online Media and Global Communication

• Type: Journal
• Language: English
• Publisher: De Gruyter Mouton
• First published: January 1, 2022
• Publication Frequency: 4 Issues per Year
• Audience: Scholars in Communication, Media Studies, Internet Studies, International Studies, International Relations

Social Media Adoption, Usage And Impact In Business-To-Business (B2B) Context: A State-Of-The-Art Literature Review

• Open access
• Published: 02 February 2021
• Volume 25 , pages 971–993, ( 2023 )

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• Yogesh K. Dwivedi 1 ,
• Elvira Ismagilova 2 ,
• Nripendra P. Rana 2 &
• Ramakrishnan Raman 3  

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Social media plays an important part in the digital transformation of businesses. This research provides a comprehensive analysis of the use of social media by business-to-business (B2B) companies. The current study focuses on the number of aspects of social media such as the effect of social media, social media tools, social media use, adoption of social media use and its barriers, social media strategies, and measuring the effectiveness of use of social media. This research provides a valuable synthesis of the relevant literature on social media in B2B context by analysing, performing weight analysis and discussing the key findings from existing research on social media. The findings of this study can be used as an informative framework on social media for both, academic and practitioners.

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1 Introduction

The Internet has changed social communications and social behaviour, which lead to the development of new forms of communication channels and platforms (Ismagilova et al. 2017 ). Social media plays an important part in the digital transformation of businesses (Kunsman 2018 ). Digital transformation refers to the globally accelerated process of technical adaptation by companies and communities as a result of digitalisation (Sivarajah et al. 2019 ; Westerman et al. 2014 ). Web is developed from a tool used to provide passive information into the collaborative web, which allows and encourages active user engagement and contribution. If before social networks were used to provide the information about a company or brand, nowadays businesses use social media in their marketing aims and strategies to improve consumers’ involvement, relationship with customers and get useful consumers’ insights (Alalwan et al. 2017 ). Business-to-consumer (B2C) companies widely use social media as part of their digital transformation and enjoy its benefits such as an increase in sales, brand awareness, and customer engagement to name a few (Barreda et al. 2015 ; Chatterjee and Kar 2020 ; Harrigan et al. 2020 ; Kamboj et al. 2018 ; Kapoor et al. 2018 ).

From a marketing and sales research perspective, social media is defined as “the technological component of the communication, transaction and relationship building functions of a business which leverages the network of customers and prospects to promote value co-creation” (Andzulis et al. 2012 p.308). Industrial buyers use social media for their purchase as they compare products, research the market and build relationships with salesperson (Itani et al. 2017 ). Social media changed the way how buyers and sellers interact (Agnihotri et al. 2016 ) by enabling open and broad communications and cooperation between them (Rossmann and Stei 2015 ). Social media is an important facilitator of relationships between a company and customers (Agnihotri et al. 2012 ; Tedeschi 2006 ). Customers are more connected to companies, which make them more knowledgable about product selection and more powerful in buyer-seller relationships (Agnihotri et al. 2016 ). Social media also helps companies to increase business exposure, traffic and providing marketplace insight (Agnihotri et al. 2016 ; Stelzner 2011 ). As a result, the use of social media supports business decision processes and helps to improve companies’ performance (Rossmann and Stei 2015 ).

Due to digitalisation customers are becoming more informed and rely less on traditional selling initiatives (Ancillai et al. 2019 ). Buyers are relying more on digital resources and their buying process more often involves the use of social media. For example, in the research B2B buyer survey, 82% of buyers stated that social media content has a significant impact on the purchase decision (Ancillai et al. 2019 ; Minsky and Quesenberry 2016 ). As a result, these changes in consumer behaviour place high pressure on B2B salespeople and traditional sales companies (Ancillai et al. 2019 ). By using evidence from major B2B companies and consultancy report some studies claim that social media can be applied in sales to establish effective dialogues with buyers (Ancillai et al. 2019 ; Kovac 2016 ; McKinsey and Company 2015 ).

Now, business-to-business (B2B) companies started using social media as part of their digital transformation. 83% of B2B companies use social media, which makes it the most common marketing tactic (Pulizzi and Handley 2017 ; Sobal 2017 ). More than 70% of B2B companies use at least one of the “big 4” social media sites such as LinkedIn, Twitter, Facebook and YouTube. Additionally, 50% of the companies stated that social media has improved their marketing optimization and customer experience, while 25% stated that their revenue went up (Gregorio 2017 ; Sobal 2017 ). Even though B2B companies are benefitting from social media used by marketers, it is argued that research on that area is still in the embryonic stage and future research is needed (Salo 2017 ; Siamagka et al. 2015 ; Juntunen et al. 2020 ; Iannacci et al. 2020 ). There is a limited understanding of how B2B companies need to change to embrace recent technological innovations and how it can lead to business and societal transformation (Chen et al. 2012 ; Loebbecke and Picot 2015 ; Pappas et al. 2018 ).

The topic of social media in the context of B2B companies has started attracting attention from both academics and practitioners. This is evidenced by the growing number of research output within academic journals and conference proceedings. Some studies provided a comprehensive literature review on social media use by B2B companies (Pascucci et al. 2018 ; Salo 2017 ), but focused only on adoption of social media by B2B or social media influence, without providing the whole picture of the use of social media by B2B companies. Thus, this study aims to close this gap in the literature by conducting a comprehensive analysis of the use of social media by B2B companies and discuss its role in the digital transformation of B2B companies. The findings of this study can provide an informative framework for research on social media in the context of B2B companies for academics and practitioners.

The remaining sections of the study are organised as follows. Section 2 offers a brief overview of the methods used to identify relevant studies to be included in this review. Section 3 synthesises the studies identified in the previous section and provides a detailed overview. Section 4 presents weight analysis and its findings. Next section discusses the key aspects of the research, highlights any limitations within existing studies and explores the potential directions for future research. Finally, the paper is concluded in Section 6 .

2 Literature Search Method

The approach utilised in this study aligns with the recommendations in Webster and Watson ( 2002 ). This study used a keyword search-based approach for identifying relevant articles (Dwivedi et al. 2019b ; Ismagilova et al. 2020a ; Ismagilova et al. 2019 ; Jeyaraj and Dwivedi 2020 ; Williams et al. 2015 ). Keywords such as “Advertising” OR “Marketing” OR “Sales” AND TITLE (“Social Media” OR “Web 2.0” OR “Facebook” OR “LinkedIn” OR “Instagram” OR “Twitter” OR “Snapchat” OR “Pinterest” OR “WhatsApp” OR “Social Networking Sites”) AND TITLE-ABS-KEY (“B2B” OR “B to B” OR “Business to Business” OR “Business 2 Business”) were searched via the Scopus database. Scopus database was chosen to ensure the inclusion of only high quality studies. Use of online databases for conducting a systematic literature review became an emerging culture used by a number of information systems research studies (Dwivedi et al. 2019a ; Gupta et al. 2019 ; Ismagilova et al. 2020b ; Muhammad et al. 2018 ; Rana et al. 2019 ). The search resulted in 80 articles. All studies were processed by the authors in order to ensure relevance and that the research offered a contribution to the social media in the context B2B discussion. The search and review resulted in 70 articles and conference papers that formed the literature review for this study. The selected studies appeared in 33 separate journals and conference proceedings, including journals such as Industrial Marketing Management, Journal of Business and Industrial Marketing and Journal of Business Research.

3 Literature Synthesis

The studies on social media research in the context of B2B companies were divided into the following themes: effect of social media, adoption of social media, social media strategies, social media use, measuring the effectiveness of use of social media, and social media tools (see Table 1 ). The following subsections provide an overview of each theme.

3.1 Effect of Social Media

Some studies focus on the effect of social media for B2B companies, which include customer satisfaction, value creation, intention to buy and sales, building relationships with customers, brand awareness, knowledge creation, perceived corporate credibility, acquiring of new customers, salesperson performance, employee brand engagement, and sustainability (Table 2 ).

3.1.1 Customer Satisfaction

Some studies investigated how the use of social media affected customer satisfaction (Agnihotri et al. 2016 ; Ancillai et al. 2019 ; Rossmann and Stei 2015 ). For example, Agnihotri et al. ( 2016 ) investigated how the implementation of social media by B2B salesperson affects consumer satisfaction. Salesperson’s social media use is defined as a “salesperson’s utilization and integration of social media technology to perform his or her job” (Agnihotri et al. 2016 , p.2). The study used data from 111 sales professionals involved in B2B industrial selling to test the proposed hypotheses. It was found that a salesperson’s use of social media will have a positive effect on information communication, which will, in turn, lead to improved customer satisfaction with the salesperson. Also, it was investigated that information communication will be positively related to responsiveness, which impacts customer satisfaction.

Another study by Rossmann and Stei ( 2015 ) looked at the antecedents of social media use, social media use by B2B companies and their effect on customers. By using data from 362 chief information officers of B2B companies the study found the following. Social media usage of sales representative has a positive impact on customer satisfaction. Age has a negative effect on content generation. It seems that older salespeople use social media in passive ways or interacting with the customer rather than creating their own content. It was found that the quality of corporate social media strategy has a positive impact on social media usage in terms of the consumption of information, content generation, and active interaction with customers. Also, the expertise of a salesperson in the area of social media has a positive impact on social media usage.

3.1.2 Value Creation

Research in B2B found that social media can create value for customers and salesperson (Agnihotri et al. 2012 ; Agnihotri et al. 2017 ). Agnihotri et al. ( 2012 ) proposed a theoretical framework to explain the mechanisms through which salespeople’s use of social media operates to create value and propose a strategic approach to social media use to achieve competitive goals. The study draws on the existing literature on relationship marketing, task–technology fit theory, and sales service behavior to sketch a social media strategy for business-to-business sales organizations with relational selling objectives. The proposed framework describes how social media tools can help salespeople perform service behaviors (information sharing, customer service, and trust-building) leading to value creation.

Some researchers investigated the role of the salesperson in the value creation process after closing the sale. By employing salesperson-customer data within a business-to-business context, Agnihotri et al. ( 2017 ) analysed the direct effects of sales-based CRM technology on the post-sale service behaviors: diligence, information communication, inducements, empathy, and sportsmanship. Additionally, the study examines the interactive effects of sales-based CRM technology and social media on these behaviors. The results indicate that sales-based CRM technology has a positive influence on salesperson service behaviors and that salespeople using CRM technology in conjunction with social media are more likely to exhibit higher levels of SSBs than their counterparts with low social media technology use. Data were collected from 162 salespeople from India. SmartPLS was used to analyse the data.

3.1.3 Intention to Buy and Sales

Another group of studies investigated the effect of social media on the level of sales and consumer purchase intention (Ancillai et al. 2019 ; Itani et al. 2017 ; Salo 2017 ; Hsiao et al. 2020 ; Mahrous 2013 ). For example, Itani et al. ( 2017 ) used the theory of reasoned actions to develop a model that tests the factors affecting the use of social media by salesperson and its impact. By collecting data from 120 salespersons from different industries and using SmartPLS to analyse the data, it was found that attitude towards social media usefulness did not affect the use of social media. It was found that social media use positively affects competitive intelligence collection, adaptive selling behaviour, which in turn influenced sales performance. Another study by Ancillai et al. ( 2019 ) used in-depth interviews with social selling professionals. The findings suggest that the use of social media improves not only the level of sales but also affects relationship and customer performance (trust, customer satisfaction, customer referrals); and organisational performance (organisational selling performance and brand performance).

It was investigated that social media has a positive effect on the intention to purchase (Hsiao et al. 2020 ; Mahrous 2013 ). For instance, Mahrous ( 2013 ) by reviewing the literature on B2B and B2C companies concluded that social media has a significant influence on consumer buying behaviour.

3.1.4 Customer Relationships

Another group of studies focused on the effect of social media on customer relationships (Bhattacharjya and Ellison 2015 ; Gáti et al. 2018 ; Gruner and Power 2018 ; Hollebeek 2019 ; Iankova et al. 2018 ; Jussila et al. 2011 ; Kho 2008 ; Niedermeier et al. 2016 ; Ogilvie et al. 2018 ). For example, Bhattacharjya and Ellison ( 2015 ) investigated the way companies build relationships with customers by using responsive customer relationship management. The study analysed customer relationship management activities from Twitter account of a Canadian company Shopify (B2B service provider). The company uses Twitter to engage with small business customers, develops and consumers. Jussila et al. ( 2011 ), by reviewing the literature, found that social media leads to increased customer focus and understanding, increased level of customer service and decreased time-to-market.

Gáti et al. ( 2018 ) focused their research efforts on social media use in customer relationship performance, particularly in customer relations. The study investigated the adoption and impact of social media by salespeople of B2B companies. By using data of 112 salespeople from several industries the study found that the intensity of technology use positively affects attitude towards social media, which positively affects social media use. Intensive technology use in turn positively affects customer relationship performance (customer retention). PLS-SEM was applied for analysis.

Another study by Gruner and Power ( 2018 ) investigated the effectiveness of the use of multiple social media platforms in communications with customers. By using data from 208 large Australian organisations, the paper explores how companies’ investment in one form of social media impacts activity on another form of social media. A regression analysis was performed to analyse the data. It was found that widespread activities on LinkedIn, Twitter and YouTube have a negative effect on a company’s marketing activity on Facebook. Thus, having it is more effective for the company to focus on a specific social media platform in forming successful inter-organisational relationships with customers.

Hollebeek ( 2019 ) proposed an integrative S-D logic/resource-based view (RBV) model of customer engagement. The proposed model considers business customer actors and resources in driving business customer resource integration, business customer resource integration effectiveness and business customer resource integration efficiency, which are antecedents of business customer engagement. Business customer engagement, in turn, results in business customer co-creation and relationship productivity.

Niedermeier et al. ( 2016 ) investigated the use of social media among salespeople in the pharmaceutical industry in China. Also, the study investigated the impact of social media on building culturally specific Guanxi relationships-it involves the exchange of factors to build trust and connection for business purpose. By using in-depth interviews with 3 sales managers and a survey of 42 pharmaceutical sales representatives that study found that WeChat is the most common social media platform used by businesses. Also, it was found to be an important tool in building Guanxi. Future studies should focus on other industries and other types of cultural features in doing business.

Ogilvie et al. ( 2018 ) investigated the effect of social media technologies on customer relationship performance and objective sales performance by using two empirical studies conducted in the United States. The first study used 375 salespeople from 1200 B2B companies. The second study used 181 respondents from the energy solution company. It was found that social media significantly affects salesperson product information communication, diligence, product knowledge and adaptability, which in turn affect customer relationship performance. It was also found that the use of social media technologies without training on technology will not lead to good results. Thus, the results propose that companies should allocate the resources required for the proper implementation of social media strategies. Future research should examine how the personality traits of a salesperson can moderate the implementation of social media technologies.

While most of the studies focused on a single country, Iankova et al. ( 2018 ) investigated the perceived effectiveness of social media by different types of businesses in two countries. By using 449 respondents from the US and the UK businesses, it was found that social media is potentially less important, at the present time, for managing ongoing relationships in B2B organizations than for B2C, Mixed or B2B2C organizations. All types of businesses ascribe similar importance to social media for acquisition-related activities. Also it was found that B2B organizations see social media as a less effective communication channel, and to have less potential as a channel for the business.

3.1.5 Brand Awareness

Some researchers argued that social media can influence brand awareness (Ancillai et al. 2019 ; Hsiao et al. 2020 ). For instance, Hsiao et al. ( 2020 ) investigated the effect of social media in the fashion industry. By collecting 1395 posts from lookbook.nu and employing regression analysis it was found that the inclusion of national brand and private fashion brands in the post increased the level of popularity which leads to purchasing interest and brand awareness.

3.1.6 Knowledge Creation

Multiple types of collaborative web tools can help and significantly increase the collaboration and the use of the distributed knowledge inside and outside of the company (McAfee 2006 ). Kärkkäinen et al. ( 2011 ) by analysing previous literature on social media proposed that social media use has a positive effect on sharing and creation of customer information and knowledge in the case of B2B companies.

3.1.7 Corporate Credibility

Another study by Kho ( 2008 ) states the advantages of using social media by B2B companies, which include faster and more personalised communications between customer and vendor, which can improve corporate credibility and strengthen the relationships. Thanks to social media companies can provide more detailed information about their products and services. Kho ( 2008 ) also mentions that customer forums and blog comments in the B2B environment should be carefully monitored in order to make sure that inappropriate discussions are taken offline and negative eWOM communications should be addressed in a timely manner.

3.1.8 Acquiring New Customers

Meire et al. ( 2017 ) investigated the impact of social media on acquiring B2B customers. By using commercially purchased prospecting data, website data and Facebook data from beverage companies the study conducted an experiment and found that social media us an effective tool in acquiring B2B customers. Future work might assess the added value of social media pages for profitability prediction instead of prospect conversion. When a longer timeframe becomes available (e.g., after one year), the profitability of the converted prospects can be assessed.

3.1.9 Salesperson Performance

Moncrief et al. ( 2015 ) investigated the impact of social media technologies on the role of salesperson position. It was found that social media affects sales management functions (supervision, selection, training, compensation, and deployment) and salesperson performance (role, skill, and motivation). Another study by Rodriguez et al. ( 2012 ) examines the effect of social media on B2B sales performance by using social capital theory and collecting data from 1699 B2B salespeople from over 25 different industries. By employing SEM AMOS, the study found that social media usage has a positive significant relationship with selling companies’ ability to create opportunities and manage relationships. The study also found that social media usage has a positive and significant relationship with sales performance (based on relational measurers of sales that focus on behaviours that strengthen the relationship between buyers and sellers), but not with outcome-based sales performance (reflected by quota achievement, growth in average billing size, and overall revenue gain).

3.1.10 Employee Brand Management

The study by Pitt et al. ( 2018 ) focuses on employee engagement with B2B companies on social media. By using results from Glassdoor (2315 five-star and 1983 one-star reviews for the highest-ranked firms, and 1013 five star and 1025 one-star reviews for lowest ranked firms) on employee brand engagement on social media, two key drivers of employee brand engagement by using the content analysis tool DICTION were identified-optimism and commonality. Individuals working in top-ranked companies expressed a higher level of optimism and commonality in comparison with individuals working in low-ranked companies. As a result, a 2 × 2 matrix was constructed which can help managers to choose strategies in order to increase and improve employee brand engagement. Another study by Pitt et al. ( 2017 ) focused on employee engagement of B2B companies on social media. By using a conceptual framework based on a theory of word choice and verbal tone and 6300 reviews collected from Glassdoor and analysed using DICTION. The study found that employees of highly ranked B2B companies are more positive about their employer brand and talk more optimistically about these brands. For low ranked B2B companies it was found that employees express a greater level of activity, certainty, and realism. Also, it was found that they used more aggressive language.

3.1.11 Sustainability

Sustainability refers to the strategy that helps a business “to meet its current requirements without compromising its ability to meet future needs” (World Commission Report on Environment and Development 1987 , p 41). Two studies out of 70 focused on the role of social media for B2B sustainability (Sivarajah et al. 2019 ; Kasper et al. 2015 ). For example, Sivarajah et al. ( 2019 ) argued that big data and social media within a participatory web environment to enable B2B organisations to become profitable and remain sustainable through strategic operations and marketing related business activities.

Another study by Kasper et al. ( 2015 ) proposed the Social Media Matrix which helps companies to decide which social media activities to execute based on their corporate and communication goals. The matrix includes three parts. The first part is focusing on social media goals and task areas, which were identified and matched. The second part consists of five types of social media activities (content, interaction/dialog, listening and analysing, application and networking). The third part provides a structure to assess the suitability of each activity type on each social media platform for each goal. The matrix was successfully tested by assessing the German B2B sector by using expert interviews with practitioners.

Based on the reviewed studies, it can be seen that if used appropriately social media have positive effect on B2B companies before and after sales, such as customer satisfaction, value creation, intention to buy and sales, customer relationships, brand awareness, knowledge creation, corporate credibility, acquiring new customers, salesperson performance, employee brand management, and sustainability. However, limited research is done on the negative effect of social media on b2b companies.

3.2 Adoption of Social Media

Some scholars investigated factors affecting the adoption of social media by B2B companies (Buratti et al. 2018 ; Gáti et al. 2018 ; Gazal et al. 2016 ; Itani et al. 2017 ; Kumar and Möller 2018 ; Lacka and Chong 2016 ). For instance, Lacka and Chong ( 2016 ) investigated factors affecting the adoption of social media by B2B companies from different industries in China. The study collected the data from 181 respondents and used the technology acceptance model with Nielsen’s model of attributes of system acceptability as a theoretical framework. By using SEM AMOS for analysis the study found that perceived usability, perceived usefulness, and perceived utility positively affect adoption and use of social media by B2B marketing professionals. The usefulness is subject to the assessment of whether social media sites are suitable means through which marketing activities can be conducted. The ability to use social media sites for B2B marketing purposes, in turn, is due to those sites learnability and memorability attributes.

Another study by Müller et al. ( 2018 ) investigated factors affecting the usage of social media. By using survey data from 100 Polish and 39 German sensor suppliers, it was found that buying frequency, the function of a buyer, the industry sector and the country does not affect the usage of social media in the context of sensor technology from Poland and Germany. The study used correlation analysis and ANOVA.

Lashgari et al. ( 2018 ) studied the adoption and use of social media by using face-to-face interviews with key managers of four multinational corporations and observations from companies’ websites and social media platforms. It was found that that the elements essential in forming the B2B firm’s social media adoption strategies are content (depth and diversity), corresponding social media platform, the structure of social media channels, the role of moderators, information accessibility approaches (public vs. gated-content), and online communities. These elements are customized to the goals and target group the firm sets to pursue. Similarly, integration of social media into other promotional channels can fall under an ad-hoc or continuous approach depending on the scope and the breadth of the communication plan, derived from the goal.

Similar to Lashgari et al. ( 2018 ), Shaltoni ( 2017 ) used data from managers. The study applied technology organisational environmental framework and diffusion of innovations to investigate factors affecting the adoption of social media by B2B companies. By using data from marketing managers or business owners of 480 SMEs, the study found that perceived relative advance, perceive compatibility, organizational innovativeness, competitor pressure, and customer pressure influence the adoption of social media by B2B companies. The findings also suggest that many decision-makers in B2B companies think that Internet marketing is not beneficial, as it is not compatible with the nature of B2B markets.

Buratti et al. ( 2018 ) investigated the adoption of social media by tanker shipping companies and ocean carriers. By using data from 60 companies the following was found. LinkedIn is the most used tool, with a 93.3% adoption rate. Firm size emerges as a predictor of Twitter’s adoption: big companies unveil a higher attitude to use it. Finally, the country of origin is not a strong influential factor in the adoption rate. Nonetheless, Asian firms clearly show a lower attitude to join SM tools such as Facebook (70%) and LinkedIn (86.7%), probably also due to governmental web restrictions imposed in China. External dimensions such as the core business, the firm size, the geographic area of origin, etc., seem to affect network wideness. Firm size, also, discriminates the capacity of firms to build relational networks. Bigger firms create networks larger than small firms do. Looking at geographical dimensions, Asian firms confirm to be far less active on SM respect to European and North American firms. Finally, the study analyzed the format of the contents disclosed by sample firms, observing quite limited use of photos and videos: in the sample industries, informational contents seem more appropriate for activating a dialogue with stakeholders and communication still appears formulated in a very traditional manner. Preliminary findings suggest that companies operating in conservative B2B services pursue different strategic approaches toward SMM and develop ad hoc communication tactics. Nonetheless, to be successful in managing SM tools, a high degree of commitment and a clear vision concerning the role of SM within communication and marketing strategy is necessary.

Gazal et al. ( 2016 ) investigated the adoption and measuring of the effectiveness of social media in the context of the US forest industry by using organisational-level adoption framework and TAM. By using data from 166 companies and performing regression analysis, the following results were received. Years in business, new sales revenue, product type, amount of available information on a company website, perceived importance of e-commerce and perceived ease of use of social media significantly affected social media use. Also, it was found that companies’ strategies and internal resources and capabilities and influence a company’s decision to adopt social media. Also, it was found that 94 of respondents do not measure the ROI from social media use. The reason is that the use of social media in marketing is relatively new and companies do not possess the knowledge of measuring ROI from the use of social media. Companies mostly use quantitative metrics (number of site visits, number of social network friends, number of comments and profile views) and qualitative metrics (growth of relationships with the key audience, audience participation, moving from monologue to dialogue with consumers. Facebook was found to be the most effective social media platform reported by the US forest industry.

The study by Kumar and Möller ( 2018 ) investigated the role of social media for B2B companies in their recruitment practices. By using data from international B2B company with headquarter in Helsinki, Finland comprised of 139 respondents it was found that brand familiarity encourages them to adopt social media platforms for a job search; however, the effect of the persuasiveness of recruitment messages on users’ adoption of social media platforms for their job search behavior is negative. The study used correlation analysis and descriptive analysis to analyse the data.

Nunan et al. ( 2018 ) identified areas for future research such as patterns of social media adoption, the role of social media platforms within the sales process, B2B consumer engagement and social media, modeling the ROI of social media, and the risks of social media within B2B sales relationships.

The study by Pascucci et al. ( 2018 ) conducted a systematic literature review on antecedents affecting the adoption and use of social media by B2B companies. By reviewing 29 studies published in academic journal and conferences from 2001 to 2017, the study identified external (pressure from customers, competitors, availability of external information about social media) and internal factors (personal characteristics -managers age, individual commitment, perceptions of social media-perceived ease of use, perceived usefulness, perceived utility), which can affect adoption of social media.

The study by Siamagka et al. ( 2015 ) aims to investigate factors affecting the adoption of social media by B2B organisations. The conceptual model was based on the technology acceptance model and the resource-based theory. AMOS software and Structural equation modelling were employed to test the proposed hypotheses. By using a sample of 105 UK companies, the study found that perceived usefulness of social media is influenced by image, perceived ease of use and perceived barriers. Also, it was found that social media adoption is significantly determined by organisational innovativeness and perceived usefulness. Additionally, the study tested the moderating role of organisational innovativeness and found that it does not affect the adoption of social media by B2B organisations. The study also identified that perceived barriers to SNS (uncertainty about how to use SNS to achieve objectives, employee’s lack of knowledge about SNS, high cost of investment needed to adopt the technology) have a negative impact on perceived usefulness of social media by B2B organisations. The study also used nine in-depth interviews with B2B senior managers and social media specialists about adoption of social media by B2B. It was found that perceived pressure from stakeholders influences B2B organisations’ adoption intention of social media. Future research should test it by using quantitative methods.

While most of the studies focused on the antecedents of social media adoption by B2B companies, Michaelidou et al. ( 2011 ) investigated the usage, perceived barriers and measuring the effectiveness of social media. By using data from 92 SMEs the study found that over a quarter of B2B SMEs in the UK are currently using SNS to achieve brand objectives, the most popular of which is to attract new customers. The barriers that prevent SMEs from using social media to support their brands were lack of staff familiarity and technical skills. Innovativeness of a company determined the adoption of social media. It was found that most of the companies do not evaluate the effectiveness of their SNS in supporting their brand. The most popular measures were the number of users joining the groups/discussion and the number of comments made. The findings showed that the size of the company does not influence the usage of social media for small and medium-sized companies. Future research should investigate the usage of social media in large companies and determine if the size can have and influence on the use. The benefits of using social media include increasing awareness and communicating the brand online. B2B companies can employ social media to create customer value in the form of interacting with customers, as well as building and fostering customer relationships. Future research should investigate the reasons why most of the users do not assess the effectiveness of their SNS. Future research should also investigate how the attitude towards technology can influence the adoption of social media.

Based on the reviewed studies it can be seen that the main factors affecting the adoption of social media by B2B companies are perceived usability, technical skills of employees, pressure from stakeholders, perceived usefulness and innovativeness.

3.3 Social Media Strategies

Another group of studies investigated types of strategies B2B companies apply (Cawsey and Rowley 2016 ; Huotari et al. 2015 ; Kasper et al. 2015 ; McShane et al. 2019 ; Mudambi et al. 2019 ; Swani et al. 2013 ; Swani et al. 2014 ; Swani et al. 2017 ; Watt 2010 ). For example, Cawsey and Rowley ( 2016 ) focused on the social media strategies of B2B companies. By conducting semi-structured interviews with marketing professionals from France, Ireland, the UK and the USA it was found that enhancing brand image, extending brand awareness and facilitating customer engagement were considered the most common social media objective. The study proposed the B2B social media strategy framework, which includes six components of a social media strategy: 1) monitoring and listening 2) empowering and engaging employees 3) creating compelling content 4) stimulating eWOM 5) evaluating and selecting channels 6) enhancing brand presence through integrating social media.

Chirumalla et al. ( 2018 ) focused on the social media engagement strategies of manufacturing companies. By using semi-structured interviews (36), observations (4), focus group meetings (6), and documentation, the study developed the process of social media adoption through a three-phase engagement strategy which includes coordination, cooperation, and co-production.

McShane et al. ( 2019 ) proposed social media strategies to influence online users’ engagement with B2B companies. Taking into consideration fluency lens the study analysed Twitter feeds of top 50 social B2B brands to examine the influence of hashtags, text difficulty embedded media and message timing on user engagement, which was evaluated in terms of likes and retweets. It was found that hashtags and text difficulty are connected to lower levels of engagement while embedded media such as images and videos improve the level of engagement.

Swani et al. ( 2014 ) investigate the use of Twitter by B2B and B2C companies and predict factors that influence message strategies. The study conducted a longitudinal content analysis by collecting 7000 tweets from Fortune 500 companies. It was found that B2B and B2C companies used different message appeals, cues, links and hashtags. B2B companies tend to use more emotional than functional appeals. It was found that B2B and B2C companies do not use hard-sell message strategies.

Another study by Swani et al. ( 2013 ) aimed to investigate message strategies that can help in promoting eWOM activity for B2B companies. By applying content analysis and hierarchical linear modeling the study analysed 1143 wall post messages from 193 fortune 500 Facebook accounts. The study found that B2B account posts will be more effective if they include corporate brand names and avoid hard sell or explicitly commercial statement. Also, companies should use emotional sentiment in Facebook posts.

Huotari et al. ( 2015 ) aimed to investigate how B2B marketers can influence content creation in social media. By conducting four face-to-face interviews with B2B marketers, it was found that a B2B company can influence content creation in social media directly by adding new content, participating in a discussion and removing content through corporate user accounts and controlling employees social media behaviour. Also, it can influence it indirectly by training employees to create desired content and perfuming marketing activities that influence other users to create content that is favorable for the company.

Most of the studies investigated the strategies and content of social media communications of B2B companies. However, the limited number of studies investigated the importance of CEO engagement on social media in the company’s strategies. Mudambi et al. ( 2019 ) emphasise the importance of the CEO of B2B companies to be present and active on social media. The study discusses the advantages of social media presence for the CEO and how it will benefit the company. For example, one of the benefits for the CEO can be perceived as being more trustworthy and effective than non-social CEOs, which will benefit the company in increased customer trust. Mudambi et al. ( 2019 ) also discussed the platforms the CEO should use and posting frequencies depending on the content of the post.

From the above review of the studies, it can be seen that B2B companies social media strategies include enhancing brand image, extending brand awareness and facilitating customer engagement. Companies use various message strategies, such as using emotional appeal, use of brand names, and use of hashtags. Majority of the companies avoid hard sell or explicitly commercial statement.

3.4 Social Media Use

Studies investigated the way how companies used social media and factors affecting the use of social media by B2B (Andersson et al. 2013 ; Bernard 2016 ; Bolat et al. 2016 ; Denktaş-Şakar and Sürücü 2018 ; Dyck 2010 ; Guesalaga 2016 ; Habibi et al. 2015 ). For example, Vasudevan and Kumar ( 2018 ) investigated how B2B companies use social media by analysing 325 brand posts of Canon India, Epson India, and HP India on Linkedin, Facebook, and Twitter. By employing content analysis the study found that most of the posts had a combination of text and message. More than 50% of the posts were about product or brand-centric. The study argued that likes proved to be an unreliable measure of engagement, while shares were considered a more reliable metric. The reason was that likes had high spikes when brand posts were boosted during promotional activities.

Andersson and Wikström ( 2017 ) used case studies of three B2B companies to investigate reasons for using social media. It was found that companies use social media to enhance customer relationships, support sales and build their brands. Also, social media is used as a recruiting tool, a seeking tool, and a product information and service tool.

Bell and Shirzad ( 2013 ) aimed to conduct social media use analysis in the context of pharmaceutical companies. The study analysed 54,365 tweets from the top five pharmaceutical companies. The study analysed the popular time slots, the average number of positive and negative tweets and its content by using Nvivo9.

Bernard ( 2016 ) aims to examine how chief marketing officers use social media. By using case studies from IBM experience with social media it was found that B2B CMO’s are not ready to make use of social media. It was proposed that social media can be used for after-sales service, getting sales leads, engaging with key influencers, building the company’s reputation and enhancing the industry status of key individuals. B2B firms need to exploit the capabilities of processing massive amounts of data to get the most from social media.

Bolat et al. ( 2016 ) explore how companies apply mobile social media. By employing a grounded theory approach to analyse interviews from 26 B2B company representatives from UK advertising and marketing sector companies. It was found that companies use social media for branding, sensing market, managing relationships, and developing content.

Denktaş-Şakar and Sürücü ( 2018 ) investigated how social media usage influence stakeholder engagement focusing on the corporate Facebook page of 30 3PLs companies. In total 1532 Facebook posts were analysed. It was found that the number of followers, post sharing frequency, negatively affect stakeholder engagement. It was found that content including photos facilitates more stakeholder engagement (likes, comment, share) in comparison with other forms. Vivid posts and special day celebration posts strengthen relationships with stakeholders.

Dyck ( 2010 ) discussed the advantages of using social media for the device industry. Social media can be used for product innovation and development, to build a team and collaborate globally. Also, there is an opportunity to connect with all of the stakeholders needed in order to deliver the device to the market. Additionally, it provides to receive feedback from customers (doctors, hospitals) in real-time.

The study by Guesalaga ( 2016 ) draws on interactional psychology theory to propose and test a model of usage of social media in sales, analysing individual, organizational, and customer-related factors. It was found that organizational competence and commitment to social media are key determinants of social media usage in sales, as well as individual commitment. Customer engagement with social media also predicts social media usage in sales, both directly and (mostly) through the individual and organizational factors analysed, especially organizational competence and commitment. Finally, the study found evidence of synergistic effects between individual competence and commitment, which is not found at the organizational level. The data obtained by surveying 220 sales executives in the United States were analysed using regression analysis.

Habibi et al. ( 2015 ) proposed a conceptual model for the implementation of social media by B2B companies. Based on existing B2B marketing, social media and organisational orientational literature the study proposed that four components of electronic market orientation (philosophical, initiation, implementation and adoption) address different implementation issues faced in implementing social media.

Katona and Sarvary ( 2014 ) presented a case of using social media by Maersk-the largest container shipping company in the world. The case provided details on the program launch and the integration strategy which focused on integrating the largest independent social media operation into the company’s broader marketing efforts.

Moore et al. ( 2013 ) provided insights into the understanding of the use of social media by salespersons. 395 salespeople in B2B and B2C markets, utilization of relationship-oriented social media applications are presented and examined. Overall, findings show that B2B practitioners tend to use media targeted at professionals whereas their B2C counterparts tend to utilize more sites targeted to the general public for engaging in one-on-one dialogue with their customers. Moreover, B2B professionals tend to use relationship-oriented social media technologies more than B2C professionals for the purpose of prospecting, handling objections, and after-sale follow-up.

Moore et al. ( 2015 ) investigated the use of social media between B2B and B2C salespeople. By using survey data from 395 sales professionals from different industries they found that B2B sales managers use social selling tools significantly more frequently than B2C managers and B2C sales representatives while conducting sales presentations. Also, it was found that B2B managers used social selling tools significantly more frequently than all sales representatives while closing sales.

Müller et al. ( 2013 ) investigated social media use in the German automotive market. By using online analysis of 10 most popular car manufacturers online social networks and surveys of six manufacturers, 42 car dealers, 199 buyers the study found that social media communication relations are widely established between manufacturers and (prospective) buyers and only partially established between car dealers and prospective buyers. In contrast to that, on the B2B side, social media communication is rarely used. Social Online Networks (SONs) are the most popular social media channels employed by businesses. Manufacturers and car dealers focus their social media engagement, especially on Facebook. From the perspective of prospective buyers, however, forums are the most important source of information.

Sułkowski and Kaczorowska-Spychalska ( 2016 ) investigated the adoption of social media by companies in the Polish textile-clothing industry. By interviewing seven companies representatives of small and medium-sized enterprises the study found that companies started implementing social media activities in their marketing activities.

Vukanovic ( 2013 ) by reviewing previous literature on social media outlined advantages of using social media for B2B companies, which include: increase customer loyalty and trust, building and improving corporate reputation, facilitating open communications, improvement in customer engagement to name a few.

Keinänen and Kuivalainen ( 2015 ) investigated factors affecting the use of social media by B2B customers by conducting an online survey among 82 key customer accounts of an information technology service company. Partial least squares path modelling was used to analysed the proposed hypotheses. It was found that social media private use, colleague support for using SM, age, job position affected the use of social media by B2B customers. The study also found that corporate culture, gender, easiness to use, and perception of usability did not affect the use of social media by B2B customers.

By using interviews and survey social media research found that mostly B2B companies use social media to enhance customer relationships, support sales, build their brands, sense market, manage relationships, and develop content. Additionally, some companies use it social media as a recruitment tool. The main difference between B2B and B2C was that B2B sales managers use social selling tools significantly more frequently than B2C managers.

3.5 Measuring the Effectiveness of Social Media

It is important for a business to be able to measure the effectiveness of social media by calculating return on investment (ROI). ROI is the relationship between profit and the investment that generate that profit. Some studies focused on the ways B2B companies can measure ROI and the challenges they face (Gazal et al. 2016 ; Michaelidou et al. 2011 ; Vasudevan and Kumar 2018 ). For example, Gazal et al. ( 2016 ) investigated the adoption and measuring of the effectiveness of social media in the context of the US forest industry by using organisational-level adoption framework and TAM. By using data from 166 companies it was found that 94% of respondents do not measure the ROI from social media use. The reason is that the use of social media in marketing is relatively new and companies do not possess the knowledge of measuring ROI from the use of social media. Companies mostly use quantitative metrics (number of site visits, number of social network friends, number of comments and profile views) and qualitative metrics (growth of relationships with the key audience, audience participation, moving from monologue to dialogue with consumers).

Another study by Michaelidou et al. ( 2011 ) found that most of the companies do not evaluate the effectiveness of their SNS in supporting their brand. The most popular measures were the number of users joining the groups/discussion and the number of comments made.

Vasudevan and Kumar ( 2018 ) investigated how B2B companies use social media and measure ROI from social media by analysing 325 brand posts of Canon India, Epson India, and HP India on Linkedin, Facebook, and Twitter. By employing content analysis the study found that most of the post has a combination of text and message. More than 50% of the posts were about product or brand-centric. The study argued that likes proved to be an unreliable measure of engagement, while shares were considered a more reliable metric. The reason was that likes had high spikes when brand posts were boosted during promotional activities. Future research should conduct longitudinal studies.

By reviewing the above studies, it can be concluded that companies still struggle to find ways of measuring ROI and applying correct metrics. By gaining knowledge in how to measure ROI from social media activities, B2B companies will be able to produce valuable insights leading to better marketing strategies (Lal et al. 2020 ).

3.6 Social Media Tools

Some studies proposed tools that could be employed by companies to advance their use of social media. For example, Mehmet and Clarke ( 2016 ) proposed a social semiotic multimodal (SSMM) framework that improved the analysis of social media communications. This framework employs multimodal extensions to systemic functional linguistics enabling it to be applying to analysing non-language as well as language constituents of social media messages. Furthermore, the framework also utilises expansion theory to identify, categorise and analyse various marketing communication resources associated with marketing messages and also to reveal how conversations are chained together to form extended online marketing conversations. This semantic approach is exemplified using a Fairtrade Australia B2B case study demonstrating how marketing conversations can be mapped and analysed. The framework emphasises the importance of acknowledging the impact of all stakeholders, particularly messages that may distract or confuse the original purpose of the conversation.

Yang et al. ( 2012 ) proposed the temporal analysis technique to identify user relationships on social media platforms. The experiment was conducted by using data from Digg.com . The results showed that the proposed techniques achieved substantially higher recall but not very good at precision. This technique will help companies to identify their future consumers based on their user relationships.

Based on the literature review, it can be seen that B2B companies can benefit by using the discussed tools. However, it is important to consider that employee should have some technical skills and knowledge to use these tools successfully. As a result, companies will need to invest some resources in staff training.

4 Weight Analysis

Weight analysis enables scrutiny of the predictive power of independent variables in studied relationships and the degree of effectiveness of the relationships (Jeyaraj et al. 2006 ; Rana et al. 2015 ; Ismagilova et al. 2020a ). The results of weight analysis are depicted in Table 3 providing information about an independent variable, dependent variable, number of significant relationships, number of non-significant relationships, the total number of relationships and weight. To perform weight analysis, the number of significant relationships was divided by the total number of analysed relationships between the independent variable and the dependent variable (Jeyaraj et al. 2006 ; Rana et al. 2015 ). For example, the weight for the relationship between attitude towards social media and social media is calculated by dividing ‘1’ (the number of significant relationships) by ‘2’ (the total number of relationships) which equals 0.5.

A predictor is defined as well-utilised if it was examined five or more times, otherwise, it is defined as experimental. It can be seen from Table 3 that all relationships were examined less than five times. Thus all studied predictors are experimental. The predictor is defined as promising when it has been examined less than five times by existing studies but has a weight equal to ‘1’ (Jeyaraj et al. 2006 ). From the predictors affecting the adoption of social media, it can be seen that two are promising, technical skills of employees and pressure from stakeholders. Social media usage is a promising predictor for acquiring new customers, sales, stakeholder engagement and customer satisfaction. Perceived ease of use and age of salesperson are promising predictors of social media usage. Even though this relationship was found to be significant every time it was examined, it is suggested that this variable, which can also be referred to as experimental, will need to be further tested in order to qualify as the best predictor. Another predictor, average rating of product/service, was examined less than five times with a weight equal to 0.75, thus it is considered as an experimental predictor.

Figure 1 shows the diagrammatic representation of the factors affecting different relationships in B2B social media with their corresponding weights, based on the results of weight analysis. The findings suggest that promising predictors should be included in further empirical studies to determine their overall performance.

Diagrammatic representation of results of weight analysis. Note: experimental predictors

It can be seen from Fig. 1 that social media usage is affected by internal (e.g. attitude towards social media, technical skills of employees) and external factors (e.g. pressure from stakeholders) of the company. Also, the figure depicts the effect of social media on the business (e.g. sales) and society (e.g. customer satisfaction).

5 Discussion

In reviewing the publications gathered for this paper, the following themes were identified. Some studies investigated the effect of social media use by B2B companies. By using mostly survey to collect the data from salespeople and managers, the studies found that social media has a positive effect on number of outcomes important for the business such as customer satisfaction, value creation, intention to buy and sales, customer relationships, brand awareness, knowledge creation, corporate credibility, acquiring new customers, salespersons performance, employee brand management, and sustainability. Most of the outcomes are similar to the research on social media in the context of B2C companies. However, some of the outcomes are unique for B2B context (e.g. employee brand management, company credibility). Just recently, studies started investigating the impact of the use of social media on sustainability.

Another group of studies looked at the adoption of social media by B2B companies (Buratti et al. 2018 ; Gáti et al. 2018 ; Gazal et al. 2016 ; Itani et al. 2017 ; Kumar and Möller 2018 ). The studies investigated it mostly from the perspectives of salespersons and identify some of the key factors which affect the adoption, such as innovativeness, technical skills of employees, pressure from stakeholders, perceived usefulness, and perceived usability. As these factors are derived mostly from surveys conducted with salespersons findings can be different for other individuals working in the organisation. This it is important to conduct studies that will examine factors affecting the adoption of social media across the entire organisation, in different departments. Using social media as part of the digital transformation is much bigger than sales and marketing, it encompasses the entire company. Additionally, most of the studies were cross-sectional, which limits the understanding of the adoption of social media by B2B over time depending on the outcomes and environment (e.g. competitors using social media).

Some studies looked at social media strategies of B2B companies (Cawsey and Rowley 2016 ; Huotari et al. 2015 ; Kasper et al. 2015 ; McShane et al. 2019 ; Mudambi et al. 2019 ). By employing interviews with companies’ managers and analysing its social media platforms (e.g. Twitter) it was found that most of the companies follow the following strategies: 1) monitoring and listening 2) empowering and engaging employees 3) creating compelling content 4) stimulating eWOM 5) evaluating and selecting channels 6) enhancing brand presence through integrating social media (Cawsey and Rowley 2016 ). Some studies investigated the difference between social media strategies of B2B and B2C companies. For example, a study by Swani et al. ( 2017 ) focused on effective social media strategies. By applying psychological motivation theory the study examined the key differences in B2B and B2C social media message strategies in terms of branding, message appeals, selling, and information search. The study used Facebook posts on brand pages of 280 Fortune companies. In total, 1467 posts were analysed. By using Bayesian models, the results showed that the inclusion of corporate brand names, functional and emotional appeals and information search cues increases the popularity of B2B messages in comparison with B2C messages. Also, it was found that readers of B2B content show a higher message liking rate and lower message commenting rate in comparison with readers of B2C messages.

The next group of studies looked at social media use by B2B companies (Andersson et al. 2013 ; Bernard 2016 ; Bolat et al. 2016 ; Denktaş-Şakar and Sürücü 2018 ; Dyck 2010 ; Guesalaga 2016 ; Habibi et al. 2015 ). B2B companies use social media for enhancing and managing customer relationships (Andersson and Wikström 2017 ; Bolat et al. ( 2016 ); branding (Andersson and Wikström 2017 ; Bolat et al. 2016 ), sensing market (Bolat et al. 2016 ) and co-production (Chirumalla et al. 2018 ). Additionally, it was mentioned that some of the B2B companies use social media as a recruiting tool, and tool which helps to collaborate globally (Andersson and Wikström 2017 ; Dyck 2010 ).

It is important for companies to not only use social media to achieve positive business outcomes but also it is important to measure their achievements. As a result, some of the studies focused on the measuring effectiveness of social media (Gazal et al. 2016 ; Michaelidou et al. 2011 ; Vasudevan and Kumar 2018 ). Surprisingly, it was found that not so many companies measure ROI from social media (Gazal et al. 2016 ; Michaelidou et al. 2011 ). The ones who do it mostly use quantitative metrics (number of site visits, number of social network friends, number of comments and profile views) and qualitative metrics (growth of relationships with key audience, audience participation, moving from monologue to dialogue with consumers) (Gazal et al. 2016 ). Some future studies should investigate how ROI influences the strategy of B2B companies over period of time.

The last group of studies focused on social media tools used by B2B companies (Keinänen and Kuivalainen 2015 ; Mehmet and Clarke 2016 ; Yang et al. 2012 ). By using number of social media tools (Social Semiotic Multimodal) companies are able to improve their analysis of social media communications and identify their future consumers based on their user relationships. Studies investigating barriers and factors adoption of various social media tools by B2B companies are needed.

After reviewing studies on b2B social media, weight analysis was performed. Based on the results of weight analysis the conceptual model for future studies was proposed (Fig.  2 ). It is important to note that a limited number of studies focused and empirically tested factors affecting the adoption, use, and effect of social media. As a result, identified factors were considered as experimental (examined less than five times). It is too early to label these experimental predictors as worst or best, thus their further investigation is encouraged.

Social media impact on digital transformation and sustainable societies

Additionally, our review of the literature on B2B social media identified dominant research methods used by scholars. Qualitative and quantitative techniques were used by most of these studies. Closer analysis of 70 publications reviewed in this study revealed the multiple techniques applied for gathering data. Quantitative methods used in the studies mostly used surveys (see Table 4 ).

The data was mostly gathered from salespersons, managers and data from social media platforms (e.g. Twitter, Facebook). Just a limited number of studies employed consumer reported data (see Table 5 ).

On the other hand, publications using qualitative methods mainly used interviews and web scraping for the collection of the required data. To analyse the data studies used a variety of techniques including SEM, regression analysis and content analysis being one of the most used (see Table 6 ).

5.1 Digital Transformation and Sustainability Model

Based on the conducted literature review and adapting the model by Pappas et al. ( 2018 ) Fig. 2 presents the digital transformation and sustainability model in the context of B2B companies, which conceptualise the social media ecosystems, and the factors that need to collaborate to enable the use of social media towards the achievement of digital transformation and the creation of sustainable societies. The model comprises of social media stakeholders, the use of social media by B2B companies, and effect of social media on business and society.

5.1.1 Social Media Stakeholders

Building on the discussion and model provided by Pappas et al. ( 2018 ), this paper posits that the social media ecosystem comprises of the data stakeholders (company, society), who engage on social media (posting, reading, using information from social media). The use of social media by different stakeholders will lead to different effects affecting companies, customers and society. This is an iterative process based on which the stakeholders use their experience to constantly improve and evolve their use of social media, which has impacts on both, business and society. The successful implementation of this process is key to digital transformation and the creation of sustainable societies. Most of the current studies (Andersson et al. 2013 ; Bernard 2016 ; Bolat et al. 2016 ; Denktaş-Şakar and Sürücü 2018 ; Dyck 2010 ; Guesalaga 2016 ) focus mostly on the company as a stakeholder. However, more research is needed on other types of stakeholders (e.g. society).

5.1.2 Use of Social Media by B2B Companies

Social media affects not only ways how companies connect with their clients, but it is also changing their business models, the way how the value is delivered and profit is made. To successfully implement and use social media, B2B companies need to consider various social media tools, antecedents/barriers of its adoption, identify suitable social media strategies which are in line with the company’s overall strategy, and measure effectiveness of the use of social media. There are various factors that affect the use of social media by B2B companies. The study found that social media usage is influenced by perceived ease of use, adoption of social media, attitude towards social media and age of salesperson.

The majority of the studies focus on the management of the marketing department. However, digital transformation is much bigger than just marketing as it encompasses the entire organisation. As a result, future studies should look like the entire organisation and investigate barriers and factors affecting the use of social media.

It is crucial for companies to design content which will be noticed on social media by their potential, actual and former customers. Social media content should be interesting and offer some beneficial information, rather than just focus on services the company provides. Companies could use fresh views on relevant industry news, provide information how they are contributing to society and environment, include humour in their posts, share information about the team, make it more personal. It is also useful to use images, infographics, and video content.

It is also important for companies to measure digital marketing actions. More studies are needed on how to isolate the impact of specific media marketing actions to demonstrate their impact on the desired business outcomes (Salo 2017 ). Thus, future studies can consider how particular social media channels (e.g. Facebook, LinkedIn) in a campaign of a new product/ service influence brand awareness and sales level. Also, a limited number of studies discussed the way B2B companies can measure ROI. Future research should investigate how companies can measure intangible ROI, such as eWOM, brand awareness, and customer engagement (Kumar and Mirchandani 2012 ). Also, future research should investigate the reasons why most of the users do not assess the effectiveness of their SNS. Furthermore, most of the studies focused on likes, shares, and comments to evaluate social media engagement. Future research should focus on other types of measures. More research needs considering the impact of legislation on the use of social media by companies. Recent B2B studies did not consider recent legislation (General Data Protection Regulation 2018 ) in the context B2B (Sivarajah et al. 2019 ).

5.1.3 Effect of Social Media on Business and Society

Social media plays an important part in the company’s decision-making process. Social media can bring positive changes into company, which will result in improving customer satisfaction, value creation, increase in sales, building relationships with customers, knowledge creation, improve the perception of corporate credibility, acquisition of new customers, and improve employment brand engagement. Using information collected from social media can help companies to have a set of reliable attributes that comprise social, economic and environmental aspects in their decision-making process (Tseng 2017 ). Additionally, by using social media B2B companies can provide information to other stakeholders on their sustainability activities. By using data from social media companies will be able to provide products and services which are demanded by society. It will improve the quality of life and result in less waste. Additionally, social media can be considered as a tool that helps managers to integrate business practices with sustainability (Sivarajah et al. 2019 ). As a result, social media use by B2B companies can lead to business and societal changes.

A limited number of studies investigated the effect of social media on word of mouth communications in the B2B context. Future research should investigate the differences and similarities between B2C and B2B eWOM communications. Also, studies should investigate how these types of communications can be improved and ways to deal with negative eWOM. It is important for companies to respond to comments on social media. Additionally, future research should investigate its perceived helpfulness by customers.

Majority of studies (Agnihotri et al. 2016 ; Ancillai et al. 2019 ; Rossmann and Stei 2015 ; Agnihotri et al. 2012 ; Agnihotri et al. 2017 ; Itani et al. 2017 ; Salo 2017 ; Bhattacharjya and Ellison 2015 ; Gáti et al. 2018 ; Gruner and Power 2018 ; Hollebeek 2019 ) investigated positive effect of social media such consumer satisfaction, consumer engagement, and brand awareness. However, it will be interesting to consider the dark side of social media use such as an excessive number of requests on social media to salespeople (Agnihotri et al. 2016 ), which can result in the reduction of the responsiveness; spread of misinformation which can damage the reputation of the company.

Studies were performed in China (Lacka and Chong 2016 ; Niedermeier et al. 2016 ), the USA (Guesalaga 2016 ; Iankova et al. 2018 ; Ogilvie et al. 2018 ), India (Agnihotri et al. 2017 ; Vasudevan and Kumar 2018 ), the UK (Bolat et al. 2016 ; Iankova et al. 2018 ; Michaelidou et al. 2011 ). It is strongly advised that future studies conduct research in other countries as findings can be different due to the culture and social media adoption rates. Future studies should pay particular attention to other emerging markets (such as Russia, Brazil, and South Africa) as they suffer from the slow adoption rate of social media marketing. Some companies in these countries still rely more on traditional media for advertising of their products and services, as they are more trusted in comparison with social media channels (Olotewo 2016 ). The majority of studies investigate the effect of social media in B2B or B2C context. Future studies should pay attention to other contexts (e.g. B2B2B, B2B2C). Another limitation of the current research on B2B companies is that most of the studies on social media in the context of B2B focus on the effect of social media use only on business outcomes. It is important for future research to focus on societal outcomes.

Lastly, most of the studies on social media in the context of B2B companies use a cross-sectional approach to collect the data. Future research can use the longitudinal approach in order to advance understanding of social media use and its impact over time.

5.2 Research Propositions

Based on the social media research in the context of B2B companies and the discussion above the following is proposed, which could serve as a foundation for future empirical work.

Social media is a powerful tool to deliver information to customers. However, social media can be used to get consumer and market insights (Kazienko et al. 2013 ). A number of studies highlighted how information obtained from a number of social media platforms could be used for various marketing purposes, such as understanding the needs and preferences of consumers, marketing potential for new products/services, and current market trends (Agnihotri et al. 2016 ; Constantinides et al. 2008 ). It is advised that future research employs a longitudinal approach to study the impact of social media use on understanding customers. Therefore, the following proposition can be formulated:

Proposition 1

Social media usage of B2B companies has a positive influence on understanding its customers.

By using social media companies can examiner valuable information on competitors. It can help to understand competitors’ habits and strategies, which can lead to the competitive advantage and help strategic planning (Dey et al. 2011 ; Eid et al. 2019 ; Teo and Choo 2001 ). It is advised that future research employs a longitudinal approach to study the impact of social media use on understanding its competitors. As a result, using social media to understand customers and competitors can create business value (Mikalef et al. 2020a ) for key stakeholders and lead to positive changes in the business and societies. The above discussion leads to the following proposition:

Proposition 2

Social media usage of B2B companies has a positive influence on understanding its competitors.

Proposition 3

Culture influences the adoption and use of social media by B2B companies.

Usage of social media can result in some positive marketing outcomes such as building new customer relationships, increasing brand awareness, and level of sales to name a few (Agnihotri et al. 2016 ; Ancillai et al. 2019 ; Dwivedi et al. 2020 ; Rossmann and Stei 2015 ). However, when social media is not used appropriately it can lead to negative consequences. If a company does not have enough resources to implement social media tools the burden usually comes on a salesperson. A high number of customer inquiries, the pressure to engage with customers on social media, and monitor communications happening on various social media platforms can result in the increased workload of a salesperson putting extra pressure (Agnihotri et al. 2016 ). As a result, a salesperson might not have enough time to engage with all the customers online promptly or engage in reactive and proactive web care. As a result, customer satisfaction can be affected as well as company reputation. To investigate the negative impact of social media research could apply novel methods for data collection and analysis such as fsQCA (Pappas et al. 2020 ), or implying eye-tracking (Mikalef et al. 2020b ). This leads to the following proposition:

Proposition 4

Inappropriate use of social media by B2B companies has a negative effect on a) customer satisfaction and b) company reputation.

According to Technology-Organisation-Environment (TOE) framework environmental context significantly affects a company’s use of innovations (Abed 2020 ; Oliveira and Martins 2011 ). Environment refers to the factors which affect companies from outside, including competitors and customers. Adopting innovation can help companies to change the rules of the competition and reach a competitive advantage (Porter and Millar 1985 ). In a competitive environment, companies have a tendency to adopt an innovation. AlSharji et al. ( 2018 ) argued that the adoption of innovation can be extended to social media use by companies. A study by AlSharji et al. ( 2018 ) by using data from 1700 SMEs operating in the United Arab Emirates found that competitive pressure significantly affects the use of social media by SMEs. It can be explained by the fact that companies could feel pressure when other companies in the industry start adopting a particular technology and as a result adopt it to remain competitive (Kuan and Chau 2001 ). Based on the above discussion, the following proposition can be formulated:

Proposition 5

Competitive pressure positively affects the adoption of social media by B2B companies.

Companies might feel that they are forced to adopt and use IT innovations because their customers would expect them to do so. Meeting customers’ expectations could result in adoption of new technologies by B2B companies. Some research studies investigated the impact of customer pressure on companies (AlSharji et al. 2018 ; Maduku et al. 2016 ). For example, a study by Maduku et al. ( 2016 ) found that customer pressure has a positive effect on SMEs adoption of mobile marketing in the context of South Africa. Future research could implement longitudinal approach to investigate how environment affects adoption of social media by B2B companies. This leads to the formulation of the following proposition:

Proposition 6

Customer pressure positively affects the adoption of social media by B2B companies.

6 Conclusion

The aim of this research was to provide a comprehensive systematic review of the literature on social media in the context of B2B companies and propose the framework outlining the role of social media in the digital transformation of B2B companies. It was found that B2B companies use social media, but not all companies consider it as part of their marketing strategies. The studies on social media in the B2B context focused on the effect of social media, antecedents, and barriers of adoption of social media, social media strategies, social media use, and measuring the effectiveness of social media. Academics and practitioners can employ the current study as an informative framework for research on the use of social media by B2B companies. The summary of the key observations provided from this literature review is the following: [i] Facebook, Twitter, and LinkedIn are the most famous social media platforms used by B2B companies, [ii] Social media has a positive effect on customer satisfaction, acquisition of new customers, sales, stakeholder engagement, and customer relationships, [iii] In systematically reviewing 70 publications on social media in the context of B2B companies it was observed that most of the studies use online surveys and online content analysis, [iv] Companies still look for ways to evaluate the effectiveness of social media, [v] Innovativeness, pressure from stakeholders, perceived usefulness, and perceived usability have a significant positive effect on companies’ adoption to use social media, [vi] Lack of staff familiarity and technical skills are the main barriers that affect the adoption of social media by B2B, [vii] Social media has an impact not only on business but also on society, [viii] There is a dark side of social media: fake online reviews, an excessive number of requests on social media to salespeople, distribution of misinformation, negative eWOM, [ix] Use of social media by companies has a positive effect on sustainability, and [x] For successful digital transformation social media should change not only the way how companies integrate it into their marketing strategies but the way how companies deliver values to their customers and conduct their business. This research has a number of limitations. First, only publications from the Scopus database were included in literature analysis and synthesis. Second, this research did not use meta-analysis. To provide a broader picture of the research on social media in the B2B context and reconcile conflicting findings of the existing studies future research should conduct a meta-analysis (Ismagilova et al. 2020c ). It will advance knowledge of the social media domain.

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Exploring the role of social media in collaborative learning the new domain of learning

• Jamal Abdul Nasir Ansari 1 &
• Nawab Ali Khan 1  

Smart Learning Environments volume  7 , Article number:  9 ( 2020 ) Cite this article

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This study is an attempt to examine the application and usefulness of social media and mobile devices in transferring the resources and interaction with academicians in higher education institutions across the boundary wall, a hitherto unexplained area of research. This empirical study is based on the survey of 360 students of a university in eastern India, cognising students’ perception on social media and mobile devices through collaborative learning, interactivity with peers, teachers and its significant impact on students’ academic performance. A latent variance-based structural equation model approach was followed for measurement and instrument validation. The study revealed that online social media used for collaborative learning had a significant impact on interactivity with peers, teachers and online knowledge sharing behaviour.

Additionally, interactivity with teachers, peers, and online knowledge sharing behaviour has seen a significant impact on students’ engagement which consequently has a significant impact on students’ academic performance. Grounded to this finding, it would be valuable to mention that use of online social media for collaborative learning facilitate students to be more creative, dynamic and research-oriented. It is purely a domain of knowledge.

Introduction

The explosion of Information and Communication Technology (ICT) has led to an increase in the volume and smoothness in transferring course contents, which further stimulates the appeasement of Digital Learning Communities (DLCs). The millennium and naughtiness age bracket were Information Technology (IT) centric on web space where individual and geopolitical disperse learners accomplished their e-learning goals. The Educause Center for Applied Research [ECAR] ( 2012 ) surveyed students in higher education mentioned that students are pouring the acceptance of mobile computing devices (cellphones, smartphones, and tablet) in Higher Education Institutions (HEIs), roughly 67% surveyed students accepted that mobile devices and social media play a vital role in their academic performance and career enhancement. Mobile devices and social media provide excellent educational e-learning opportunities to the students for academic collaboration, accessing in course contents, and tutors despite the physical boundary (Gikas & Grant, 2013 ). Electronic communication technologies accelerate the pace of their encroachment of every aspect of life, the educational institutions incessantly long decades to struggle in seeing the role of such devices in sharing the contents, usefulness and interactivity style. Adoption and application of mobile devices and social media can provide ample futuristic learning opportunities to the students in accessing course contents as well as interaction with peers and experts (Cavus & Ibrahim, 2008 , 2009 ; Kukulska-Hulme & Shield, 2008 ; Nihalani & Mayrath, 2010 ; Richardson & Lenarcic, 2008 , Shih, 2007 ). Recently Pew Research Center reported that 55% American teenage age bracket of 15–17 years using online social networking sites, i.e. Myspace and Facebook (Reuben, 2008 ). Social media, the fast triggering the mean of virtual communication, internet-based technologies changed the life pattern of young youth.

Use of social media and mobile devices presents both advantages as well as challenges, mostly its benefits seen in terms of accessing course contents, video clip, transfer of the instructional notes etc. Overall students feel that social media and mobile devices are the cheap and convenient tools of obtaining relevant information. Studies in western countries have confronted that online social media use for collaborative learning has a significant contribution to students’ academic performance and satisfaction (Zhu, 2012 ). The purpose of this research project was to explore how learning and teaching activities in higher education institutions were affected by the integration and application of mobile devices in sharing the resource materials, interaction with colleagues and students’ academic performance. The broad goal of this research was to contemporise the in-depth perspectives of students’ perception of mobile devices and social media in learning and teaching activities. However, this research paper paid attention to only students’ experiences, and their understanding of mobile devices and social media fetched changes and its competency in academic performance. The fundamental research question of this research was, what are the opinions of students on social media and mobile devices when it is integrating into higher education for accessing, interacting with peers.

A researcher of the University of Central Florida reported that electronic devices and social media create an opportunity to the students for collaborative learning and also allowed the students in sharing the resource materials to the colleagues (Gikas & Grant, 2013 ). The result of the eight Egyptian universities confirmed that social media have the significant impact on higher education institutions especially in term of learning tools and teaching aids, faculty members’ use of social media seen at a minimum level due to several barriers (internet accessibility, mobile devices etc.).

Social media and mobile devices allow the students to create, edit and share the course contents in textual, video or audio forms. These technological innovations give birth to a new kind of learning cultures, learning based on the principles of collective exploration and interaction (Selwyn, 2012 ). Social media the phenomena originated in 2005 after the Web2.0 existence into the reality, defined more clearly as “a group of Internet-based applications that build on the ideological and technological foundation of web 2.0 and allow creation and exchange of user-generated contents (Kaplan & Haenlein, 2010 ). Mobile devices and social media provide opportunities to the students for accessing resources, materials, course contents, interaction with mentor and colleagues (Cavus & Ibrahim, 2008 , 2009 ; Richardson & Lenarcic, 2008 ).

Social media platform in academic institutions allows students to interact with their mentors, access their course contents, customisation and build students communities (Greenhow, 2011a , 2011b ). 90% school going students currently utilise the internet consistently, with more than 75% teenagers using online networking sites for e-learning (DeBell & Chapman, 2006 ; Lenhart, Arafeh, & Smith, 2008 ; Lenhart, Madden, & Hitlin, 2005 ). The result of the focus group interview of the students in 3 different universities in the United States confirmed that use of social media created opportunities to the learners for collaborative learning, creating and engaging the students in various extra curriculum activities (Gikas & Grant, 2013 ).

Research background and hypotheses

The technological innovation and increased use of the internet for e-learning by the students in higher education institutions has brought revolutionary changes in communication pattern. A report on 3000 college students in the United States revealed that 90% using Facebook while 37% using Twitter to share the resource materials as cited in (Elkaseh, Wong, & Fung, 2016 ). A study highlighted that the usage of social networking sites in educational institutions has a practical outcome on students’ learning outcomes (Jackson, 2011 ). The empirical investigation over 252 undergraduate students of business and management showed that time spent on twitter and involvement in managing social lives and sharing information, course-related influences their performance (Evans, 2014 ).

Social media for collaborative learning, interactivity with teachers, interactivity with peers

Many kinds of research confronted on the applicability of social media and mobile devices in higher education for interaction with colleagues.90% of faculty members use some social media in courses they were usually teaching or professional purposes out of the campus life. Facebook and YouTube are the most visited sites for the professional outcomes, around 2/3rd of the all-faculty use some medium fora class session, and 30% posted contents for students engagement in reading, view materials (Moran, Seaman, & Tinti-Kane, 2011 ). Use of social media and mobile devices in higher education is relatively new phenomena, completely hitherto area of research. Research on the students of faculty of Economics at University of Mortar, Bosnia, and Herzegovina reported that social media is already used for the sharing the materials and exchanges of information and students are ready for active use of social networking site (slide share etc.) for educational purposes mainly e-learning and communication (Mirela Mabić, 2014 ).

The report published by the U.S. higher education department stated that the majority of the faculty members engaged in different form of the social media for professional purposes, use of social media for teaching international business, sharing contents with the far way students, the use of social media and mobile devices for sharing and the interactive nature of online and mobile technologies build a better learning environment at international level. Responses on 308 graduate and postgraduate students in Saudi Arabia University exhibited that positive correlation between chatting, online discussion and file sharing and knowledge sharing, and entertainment and enjoyment with students learning (Eid & Al-Jabri, 2016 ). The quantitative study on 168 faculty members using partial least square (PLS-SEM) at Carnegie classified Doctoral Research University in the USA confirmed that perceived usefulness, external pressure and compatibility of task-technology have positive effect on social media use, the higher the degree of the perceived risk of social media, the less likely to use the technological tools for classroom instruction, the study further revealed that use of social media for collaborative learning has a positive effect on students learning outcome and satisfaction (Cao, Ajjan, & Hong, 2013 ). Therefore, the authors have hypothesized:

H1: Use of social media for collaborative learning is positively associated with interactivity with teachers.

Additionally, Madden and Zickuhr ( 2011 ) concluded that 83% of internet user within the age bracket of 18–29 years adopting social media for interaction with colleagues. Kabilan, Ahmad, and Abidin ( 2010 ) made an empirical investigation on 300 students at University Sains Malaysia and concluded that 74% students found to be the same view that social media infuses constructive attitude towards learning English (Fig. 1 ).

Research Model

Reuben ( 2008 ) concluded in his study on social media usage among professional institutions revealed that Facebook and YouTube used over half of 148 higher education institutions. Nevertheless, a recent survey of 456 accredited United States institutions highlighted 100% using some form of social media, notably Facebook 98% and Twitter 84% for e-learning purposes, interaction with mentors (Barnes & Lescault, 2011 ).

Information and communication technology (ICT), such as web-based application and social networking sites enhances the collaboration and construction of knowledge byway of instruction with outside experts (Zhu, 2012 ). A positive statistically significant relationship was found between student’s use of a variety of social media tools and the colleague’s fellow as well as the overall quality of experiences (Rutherford, 2010 ). The potential use of social media leads to collaborative learning environments which allow students to share education-related materials and contents (Fisher & Baird, 2006 ). The report of 233 students in the United States higher educations confirmed that more recluse students interact through social media, which assist them in collaborative learning and boosting their self-confidence (Voorn & Kommers, 2013 ). Thus hypotheses as

H2: Use of social media for collaborative learning is positively associated with interactivity with peers.

Social media for collaborative learning, interactivity with peers, online knowledge sharing behaviour and students’ engagement

Students’ engagement in social media and its types represent their physical and mental involvement and time spent boost to the enhancement of educational Excellency, time spent on interaction with peers, teachers for collaborative learning (Kuh, 2007 ). Students’ engagement enhanced when interacting with peers and teacher was in the same direction, shares of ideas (Chickering & Gamson, 1987 ). Engagement is an active state that is influenced by interaction or lack thereof (Leece, 2011 ). With the advancement in information technology, the virtual world becomes the storehouse of the information. Liccardi et al. ( 2007 ) concluded that 30% students were noted to be active on social media for interaction with their colleagues, tutors, and friends while more than 52% used some social media forms for video sharing, blogs, chatting, and wiki during their class time. E-learning becomes now sharp and powerful tools in information technology and makes a substantial impact on the student’s academic performance. Sharing your knowledge will make you better. Social network ties were shown to be the best predictors of online knowledge sharing intention, which in turn associated with knowledge sharing behaviour (Chen, Chen, & Kinshuk, 2009 ). Social media provides the robust personalised, interactive learning environment and enhances in self-motivation as cited in (Al-Mukhaini, Al-Qayoudhi, & Al-Badi, 2014 ). Therefore, it was hypothesised that:

H3: Use of social media for collaborative learning is positively associated with online knowledge sharing behaviour.

Broadly Speaking social media/sites allow the students to interact, share the contents with colleagues, also assisting in building connections with others (Cain, 2008 ). In the present era, the majority of the college-going students are seen to be frequent users of these sophisticated devices to keep them informed and updated about the external affair. Facebook reported per day 1,00,000 new members join; Facebook is the most preferred social networking sites among the students of the United States as cited in (Cain, 2008 ). The researcher of the school of engineering, Swiss Federal Institute of Technology Lausanne, Switzerland, designed and developed Grasp, a social media platform for their students’ collaborative learning, sharing contents (Bogdanov et al., 2012 ). The utility and its usefulness could be seen in the University of Geneva and Tongji University at both two educational places students were satisfied and accept ‘ Grasp’ to collect, organised and share the contents. Students use of social media will interact ubiquity, heterogeneous and engaged in large groups (Wankel, 2009 ). So we hypotheses

H4: More interaction with teachers leads to higher students’ engagement.

However, a similar report published on 233 students revealed that social media assisted in their collaborative learning and self-confidence as they prefer communication technology than face to face communication. Although, the students have the willingness to communicate via social media platform than face to face (Voorn & Kommers, 2013 ). The potential use of social media tools facilitates in achieving higher-level learning through collaboration with colleagues and other renewed experts in their field (Junco, Heiberger, & Loken, 2011 ; Meyer, 2010 ; Novak, Razzouk, & Johnson, 2012 ; Redecker, Ala-Mutka, & Punie, 2010 ). Academic self-efficacy and optimism were found to be strongly related to performance, adjustment and consequently both directly impacted on student’s academic performance (Chemers, Hu, & Garcia, 2001 ). Data of 723 Malaysian researchers confirmed that both male and female students were satisfied with the use of social media for collaborative learning and engagement was found positively affected with learning performance (Al-Rahmi, Alias, Othman, Marin, & Tur, 2018 ). Social media were seen as a powerful driver for learning activities in terms of frankness, interactivity, and friendliness.

Junco et al. ( 2011 ) conducted research on the specific purpose of the social media; how Twitter impacted students’ engagement, found that it was extent discussion out of class, their participation in panel group (Rodriguez, 2011 ). A comparative study conducted by (Roblyer, McDaniel, Webb, Herman, & Witty, 2010 ) revealed that students were more techno-oriented than faculty members and more likely using Facebook and such similar communication technology to support their class-related task. Additionally, faculty members were more likely to use traditional techniques, i.e. email. Thus hypotheses framed is that:

H5: More interaction with peers ultimately leads to better students’ engagement.

Social networking sites and social media are closely similar, which provide a platform where students can interact, communicate, and share emotional intelligence and looking for people with other attitudes (Gikas & Grant, 2013 ). Facebook and YouTube channel use also increased in the skills/ability and knowledge and outcomes (Daniel, Isaac, & Janet, 2017 ). It was highlighted that 90% of faculty members were using some sort of social media in their courses/ teaching. Facebook was the most visited social media sites as per study, 40% of faculty members requested students to read and views content posted on social media; majority reports that videos, wiki, etc. the primary source of acquiring knowledge, social networking sites valuable tool/source of collaborative learning (Moran et al., 2011 ). However, more interestingly, in a study which was carried out on 658 faculty members in the eight different state university of Turkey, concluded that nearly half of the faculty member has some social media accounts.

Further reported that adopting social media for educational purposes, the primary motivational factor which stimulates them to use was effective and quick means of communication technology (Akçayır, 2017 ). Thus hypotheses formulated is:

H6: Online knowledge sharing behaviour is positively associated with the students’ engagement.

Using multiple treatment research design, following act-react to increase students’ academic performance and productivity, it was observed when self–monitoring record sheet was placed before students and seen that students engagement and educational productivity was increased (Rock & Thead, 2007 ). Student engagement in extra curriculum activities promotes academic achievement (Skinner & Belmont, 1993 ), increases grade rate (Connell, Spencer, & Aber, 1994 ), triggering student performance and positive expectations about academic abilities (Skinner & Belmont, 1993 ). They are spending time on online social networking sites linked to students engagement, which works as the motivator of academic performance (Fan & Williams, 2010 ). Moreover, it was noted in a survey of over 236 Malaysian students that weak association found between the online game and student’s academic performance (Eow, Ali, Mahmud, & Baki, 2009 ). In a survey of 671 students in Jordan, it was revealed that student’s engagement directly influences academic performance, also seen the indirect effect of parental involvement over academic performance (Al-Alwan, 2014 ). Engaged students are perceptive and highly active in classroom activities, ready to participate in different classroom extra activities and expose motivation to learn, which finally leads in academic achievement (Reyes, Brackett, Rivers, White, & Salovey, 2012 ). A mediated role of students engagement seen in 1399 students’ classroom emotional climate and grades (Reyes et al., 2012 ). A statistically significant relation was noticed between online lecture and exam performance.

Nonetheless, intelligence quotient, personality factors, students must be engaged in learning activities as cited in (Bertheussen & Myrland, 2016 ). The report of the 1906 students at 7 universities in Colombia confirmed that the weak correlation between collaborative learning, students faculty interaction with academic performance (Pineda-Báez et al., 2014 ) Thus, the hypothesis

H7: Student's Engagement is positively associated with the student's academic performance.

Methodology

To check the students’ perception on social media for collaborative learning in higher education institutions, Data were gathered both offline and online survey administered to students from one public university in Eastern India (BBAU, Lucknow). For the sake of this study, indicators of interactivity with peers and teachers, the items of students engagement, the statement of social media for collaborative learning, and the elements of students’ academic performance were adopted from (AL-Rahmi & Othman, 2013 ). The statement of online knowledge sharing behaviour was taken from (Ma & Yuen, 2011 ).

The indicators of all variables which were mentioned above are measured on the standardised seven-point Likert scale with the anchor (1-Strongly Disagree, to 7-Strongly Agree). Interactivity with peers was measured using four indicators; the sample items using social media in class facilitates interaction with peers ; interactivity with teachers was measured using four symbols, the sample item is using social media in class allows me to discuss with the teacher. ; engagement was measured using three indicators by using social media I felt that my opinions had been taken into account in this class ; social media for collaborative learning was measured using four indicators collaborative learning experience in social media environment is better than in a face-to-face learning environment ; students’ academic performance was measured using five signs using social media to build a student-lecturer relationship with my lecturers, and this improves my academic performance ; online knowledge sharing behaviour was assessed using five symbols the counsel was received from other colleague using social media has increased our experience .

Procedure and measurement

A sample of 360 undergraduate students was collected by convenience sampling method of a public university in Eastern India. The proposed model of study was measured and evaluated using variance based structured equation model (SEM)-a latent multi variance technique which provides the concurrent estimation of structural and measurement model that does not meet parametric assumption (Coelho & Duarte, 2016 ; Haryono & Wardoyo, 2012 ; Lee, 2007 ; Moqbel, Nevo, & Kock, 2013 ; Raykov & Marcoulides, 2000 ; Williams, Rana, & Dwivedi, 2015 ). The confirmatory factor analysis (CFA) was conducted to ensure whether the widely accepted criterion of discriminate and convergent validity met or not. The loading of all the indicators should be 0.50 or more (Field, 2011 ; Hair, Anderson, Tatham, & Black, 1992 ). And it should be statistically significant at least at the 0.05.

Demographic analysis (Table 1 )

The majority of the students in this study were females (50.8%) while male students were only 49.2% with age 15–20 years (71.7%). It could be pointed out at this juncture that the majority of the students (53.9%) in BBAU were joined at least 1–5 academic pages for their getting information, awareness and knowledge. 46.1% of students spent 1–5 h per week on social networking sites for collaborative learning, interaction with teachers at an international level. The different academic pages followed for accessing material, communication with the faculty members stood at 44.4%, there would be various forms of the social networking sites (LinkedIn, Slide Share, YouTube Channel, Researchgate) which provide the facility of online collaborative learning, a platform at which both faculty members and students engaged in learning activities.

As per report (Nasir, Khatoon, & Bharadwaj, 2018 ), most of the social media user in India are college-going students, 33% girls followed by 27% boys students, and this reports also forecasted that India is going to become the highest 370.77 million internet users in 2022. Additionally, the majority of the faculty members use smartphone 44% to connect with the students for sharing material content. Technological advantages were the pivotal motivational force which stimulates faculty members and students to exploits the opportunities of resource materials (Nasir & Khan, 2018 ) (Fig. 2 ).

Reasons for Using Social Media

When the students were asked for what reason did they use social media, it was seen that rarely using for self-promotion, very frequently using for self-education, often used for passing the time with friends, and so many fruitful information the image mentioned above depicting.

Instrument validation

The structural model was applied to scrutinize the potency and statistically significant relationship among unobserved variables. The present measurement model was evaluated using Confirmatory Factor Analysis (CFA), and allied procedures to examine the relationship among hypothetical latent variables has acceptable reliability and validity. This study used both SPSS 20.0 and AMOS to check measurement and structural model (Field, 2013 ; Hair, Anderson, et al., 1992 ; Mooi & Sarstedt, 2011 ; Norusis, 2011 ).

The Confirmatory Factor Analysis (CFA) was conducted to ensure whether the widely accepted criterion of discriminant and convergent validity met or not. The loading of all the indicators should be 0.70 or more it should be statistically significant at least at the 0.05 (Field, 2011 ; Hair, Anderson, et al., 1992 ).

CR or CA-based tests measured the reliability of the proposed measurement model. The CA provides an estimate of the indicators intercorrelation (Henseler & Sarstedt, 2013 . The benchmark limits of the CA is 0.7 or more (Nunnally & Bernstein, 1994 ). As per Table 2 , all latent variables in this study above the recommended threshold limit. Although, Average Variance Extracted (AVE) has also been demonstrated which exceed the benchmark limit 0.5. Thus all the above-specified values revealed that our instrument is valid and effective. (See Table 2 for the additional information) (Table 3 ).

In a nutshell, the measurement model clear numerous stringent tests of convergent validity, discriminant validity, reliability, and absence of multi-collinearity. The finding demonstrated that our model meets widely accepted data validation criteria. (Schumacker & Lomax, 2010 ).

The model fit was evaluated through the Chi-Square/degree of freedom (CMIN/DF), Root Mean Residual (RMR), Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), and Goodness of fit index (GFI) and Tucker-Lewis Index (TLI). The benchmark limit of the CFI, TLI, and GFI 0.90or more (Hair et al., 2016 ; Kock, 2011 ). The model study demonstrated in the table, as mentioned above 4 that the minimum threshold limit was achieved (See Table 4 for additional diagnosis).

Path coefficient of several hypotheses has been demonstrated in Fig.  3 , which is a variable par relationship. β (beta) Coefficients, standardised partial regression coefficients signify the powers of the multivariate relationship among latent variables in the model. Remarkably, it was observed that seven out of the seven proposed hypotheses were accepted and 78% of the explained variance in students’ academic performance, 60% explained variance in interactivity with teachers, 48% variance in interactivity with peers, 43% variance in online knowledge sharing behaviour and 79% variance in students’ engagement. Social media collaborative learning has a significant association with teacher interactivity(β = .693, P  < 0.001), demonstrating that there is a direct effect on interaction with the teacher by social media when other variables are controlled. On the other hand, use of social media for collaborative learning has noticed statistically significant positive relationship with peers interactivity (β = .704, p  < 0.001) meaning thereby, collaborative learning on social media by university students, leads to the high degree of interaction with peers, colleagues. Implied 10% rise in social media use for learning purposes, expected 7.04% increase in interaction with peers.

Path Diagram

Use of social media for collaborating learning has a significant positive association with online knowledge sharing behaviour (β = .583, p  < 0.001), meaning thereby that the more intense use of social media for collaborative learning by university students, the more knowledge sharing between peers and colleagues. Also, interaction with the teacher seen the significant statistical positive association with students engagement (β = .450, p  < 0.001), telling that the more conversation with teachers, leads to a high level of students engagement. Similarly, the practical interpretation of this result is that there is an expected 4.5% increase in student’s participation for every 10% increase in interaction with teachers. Interaction with peers has a significant positive association with students engagement (β = .210, p  < 0.001). Practically, the finding revealed that 10% upturn in student’s involvement, there is a 2.1% increase in peer’s interaction. There is a significant positive association between online knowledge sharing behaviour and students engagement (β = 0.247, p  < 0.001), and finally students engagement has been a statistically significant positive relationship with students’ academic performance (β = .972, p  < 0.001), this is the clear indication that more engaged students in collaborative learning via social media leads to better students’ academic performance.

Discussion and implication

There is a continuing discussion in the academic literature that use of such social media and social networking sites would facilitate collaborative learning. It is human psychology generally that such communication media technology seems only for entertainment, but it should be noted here carefully that if such communication technology would be followed with due attention prove productive. It is essential to acknowledge that most university students nowadays adopting social media communication to interact with colleagues, teachers and also making the group be in touch with old friends and even a convenient source of transferring the resources. In the present era, the majority of the university students having diversified social media community groups like Whatsapp, Facebook pages following different academic web pages to upgrade their knowledge.

Practically for every 10% rise in students’ engagement, expected to be 2.1% increase in peer interaction. As the study suggested that students engage in different sites, they start discussing with colleagues. More engaged students in collaborative learning through social media lead better students’ academic performance. The present study revealed that for every 10% increase in student’s engagement, there would be an expected increase in student academic performance at a rate of 9.72. This extensive research finding revealed that the application of online social media would facilitate the students to become more creative, dynamics and connect to the worldwide instructor for collaborative learning.

Accordingly, the use of online social media for collaborative learning, interaction with mentors and colleagues leadbetter student’s engagement which consequently affects student’s academic performance. The higher education authority should provide such a platform which can nurture the student’s intellectual talents. Based on the empirical investigation, it would be said that students’ engagement, social media communication devices facilitate students to retrieve information and interact with others in real-time regarding sharing teaching materials contents. Additionally, such sophisticated communication devices would prove to be more useful to those students who feel too shy in front of peers; teachers may open up on the web for the collaborative learning and teaching in the global scenario and also beneficial for physically challenged students. It would also make sense that intensive use of such sophisticated technology in teaching pedagogical in higher education further facilitates the teachers and students to interact digitally, web-based learning, creating discussion group, etc. The result of this investigation confirmed that use of social media for collaborative learning purposes, interaction with peers, and teacher affect their academic performance positively, meaning at this moment that implementation of such sophisticated communication technology would bring revolutionary, drastic changes in higher education for international collaborative learning (Table 5 ).

Limitations and future direction

Like all the studies, this study is also not exempted from the pitfalls, lacunas, and drawbacks. The first and foremost research limitation is it ignores the addiction of social media; excess use may lead to destruction, deviation from the focal point. The study only confined to only one academic institution. Hence, the finding of the project cannot be generalised as a whole. The significant positive results were found in this study due to the fact that the social media and mobile devices are frequently used by the university going students not only as a means of gratification but also for educational purposes.

Secondly, this study was conducted on university students, ignoring the faculty members, it might be possible that the faculty members would not have been interested in interacting with the students. Thus, future research could be possible towards faculty members in different higher education institutions. To the authors’ best reliance, this is the first and prime study to check the usefulness and applicability of social media in the higher education system in the Indian context.

Concluding observations

Based on the empirical investigation, it could be noted that application and usefulness of the social media in transferring the resource materials, collaborative learning and interaction with the colleagues as well as teachers would facilitate students to be more enthusiastic and dynamic. This study provides guidelines to the corporate world in formulating strategies regarding the use of social media for collaborative learning.

Availability of data and materials

The corresponding author declared here all types of data used in this study available for any clarification. The author of this manuscript ready for any justification regarding the data set. To make publically available of the data used in this study, the seeker must mail to the mentioned email address. The profile of the respondents was completely confidential.

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Ansari, J.A.N., Khan, N.A. Exploring the role of social media in collaborative learning the new domain of learning. Smart Learn. Environ. 7 , 9 (2020). https://doi.org/10.1186/s40561-020-00118-7

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Fake news, disinformation and misinformation in social media: a review

Esma aïmeur.

Department of Computer Science and Operations Research (DIRO), University of Montreal, Montreal, Canada

Sabrine Amri

Gilles brassard, associated data.

All the data and material are available in the papers cited in the references.

Online social networks (OSNs) are rapidly growing and have become a huge source of all kinds of global and local news for millions of users. However, OSNs are a double-edged sword. Although the great advantages they offer such as unlimited easy communication and instant news and information, they can also have many disadvantages and issues. One of their major challenging issues is the spread of fake news. Fake news identification is still a complex unresolved issue. Furthermore, fake news detection on OSNs presents unique characteristics and challenges that make finding a solution anything but trivial. On the other hand, artificial intelligence (AI) approaches are still incapable of overcoming this challenging problem. To make matters worse, AI techniques such as machine learning and deep learning are leveraged to deceive people by creating and disseminating fake content. Consequently, automatic fake news detection remains a huge challenge, primarily because the content is designed in a way to closely resemble the truth, and it is often hard to determine its veracity by AI alone without additional information from third parties. This work aims to provide a comprehensive and systematic review of fake news research as well as a fundamental review of existing approaches used to detect and prevent fake news from spreading via OSNs. We present the research problem and the existing challenges, discuss the state of the art in existing approaches for fake news detection, and point out the future research directions in tackling the challenges.

Introduction

Context and motivation.

Fake news, disinformation and misinformation have become such a scourge that Marcia McNutt, president of the National Academy of Sciences of the United States, is quoted to have said (making an implicit reference to the COVID-19 pandemic) “Misinformation is worse than an epidemic: It spreads at the speed of light throughout the globe and can prove deadly when it reinforces misplaced personal bias against all trustworthy evidence” in a joint statement of the National Academies 1 posted on July 15, 2021. Indeed, although online social networks (OSNs), also called social media, have improved the ease with which real-time information is broadcast; its popularity and its massive use have expanded the spread of fake news by increasing the speed and scope at which it can spread. Fake news may refer to the manipulation of information that can be carried out through the production of false information, or the distortion of true information. However, that does not mean that this problem is only created with social media. A long time ago, there were rumors in the traditional media that Elvis was not dead, 2 that the Earth was flat, 3 that aliens had invaded us, 4 , etc.

Therefore, social media has become nowadays a powerful source for fake news dissemination (Sharma et al. 2019 ; Shu et al. 2017 ). According to Pew Research Center’s analysis of the news use across social media platforms, in 2020, about half of American adults get news on social media at least sometimes, 5 while in 2018, only one-fifth of them say they often get news via social media. 6

Hence, fake news can have a significant impact on society as manipulated and false content is easier to generate and harder to detect (Kumar and Shah 2018 ) and as disinformation actors change their tactics (Kumar and Shah 2018 ; Micallef et al. 2020 ). In 2017, Snow predicted in the MIT Technology Review (Snow 2017 ) that most individuals in mature economies will consume more false than valid information by 2022.

Recent news on the COVID-19 pandemic, which has flooded the web and created panic in many countries, has been reported as fake. 7 For example, holding your breath for ten seconds to one minute is not a self-test for COVID-19 8 (see Fig.  1 ). Similarly, online posts claiming to reveal various “cures” for COVID-19 such as eating boiled garlic or drinking chlorine dioxide (which is an industrial bleach), were verified 9 as fake and in some cases as dangerous and will never cure the infection.

Fake news example about a self-test for COVID-19 source: https://cdn.factcheck.org/UploadedFiles/Screenshot031120_false.jpg , last access date: 26-12-2022

Social media outperformed television as the major news source for young people of the UK and the USA. 10 Moreover, as it is easier to generate and disseminate news online than with traditional media or face to face, large volumes of fake news are produced online for many reasons (Shu et al. 2017 ). Furthermore, it has been reported in a previous study about the spread of online news on Twitter (Vosoughi et al. 2018 ) that the spread of false news online is six times faster than truthful content and that 70% of the users could not distinguish real from fake news (Vosoughi et al. 2018 ) due to the attraction of the novelty of the latter (Bovet and Makse 2019 ). It was determined that falsehood spreads significantly farther, faster, deeper and more broadly than the truth in all categories of information, and the effects are more pronounced for false political news than for false news about terrorism, natural disasters, science, urban legends, or financial information (Vosoughi et al. 2018 ).

Over 1 million tweets were estimated to be related to fake news by the end of the 2016 US presidential election. 11 In 2017, in Germany, a government spokesman affirmed: “We are dealing with a phenomenon of a dimension that we have not seen before,” referring to an unprecedented spread of fake news on social networks. 12 Given the strength of this new phenomenon, fake news has been chosen as the word of the year by the Macquarie dictionary both in 2016 13 and in 2018 14 as well as by the Collins dictionary in 2017. 15 , 16 Since 2020, the new term “infodemic” was coined, reflecting widespread researchers’ concern (Gupta et al. 2022 ; Apuke and Omar 2021 ; Sharma et al. 2020 ; Hartley and Vu 2020 ; Micallef et al. 2020 ) about the proliferation of misinformation linked to the COVID-19 pandemic.

The Gartner Group’s top strategic predictions for 2018 and beyond included the need for IT leaders to quickly develop Artificial Intelligence (AI) algorithms to address counterfeit reality and fake news. 17 However, fake news identification is a complex issue. (Snow 2017 ) questioned the ability of AI to win the war against fake news. Similarly, other researchers concurred that even the best AI for spotting fake news is still ineffective. 18 Besides, recent studies have shown that the power of AI algorithms for identifying fake news is lower than its ability to create it Paschen ( 2019 ). Consequently, automatic fake news detection remains a huge challenge, primarily because the content is designed to closely resemble the truth in order to deceive users, and as a result, it is often hard to determine its veracity by AI alone. Therefore, it is crucial to consider more effective approaches to solve the problem of fake news in social media.

Contribution

The fake news problem has been addressed by researchers from various perspectives related to different topics. These topics include, but are not restricted to, social science studies , which investigate why and who falls for fake news (Altay et al. 2022 ; Batailler et al. 2022 ; Sterret et al. 2018 ; Badawy et al. 2019 ; Pennycook and Rand 2020 ; Weiss et al. 2020 ; Guadagno and Guttieri 2021 ), whom to trust and how perceptions of misinformation and disinformation relate to media trust and media consumption patterns (Hameleers et al. 2022 ), how fake news differs from personal lies (Chiu and Oh 2021 ; Escolà-Gascón 2021 ), examine how can the law regulate digital disinformation and how governments can regulate the values of social media companies that themselves regulate disinformation spread on their platforms (Marsden et al. 2020 ; Schuyler 2019 ; Vasu et al. 2018 ; Burshtein 2017 ; Waldman 2017 ; Alemanno 2018 ; Verstraete et al. 2017 ), and argue the challenges to democracy (Jungherr and Schroeder 2021 ); Behavioral interventions studies , which examine what literacy ideas mean in the age of dis/mis- and malinformation (Carmi et al. 2020 ), investigate whether media literacy helps identification of fake news (Jones-Jang et al. 2021 ) and attempt to improve people’s news literacy (Apuke et al. 2022 ; Dame Adjin-Tettey 2022 ; Hameleers 2022 ; Nagel 2022 ; Jones-Jang et al. 2021 ; Mihailidis and Viotty 2017 ; García et al. 2020 ) by encouraging people to pause to assess credibility of headlines (Fazio 2020 ), promote civic online reasoning (McGrew 2020 ; McGrew et al. 2018 ) and critical thinking (Lutzke et al. 2019 ), together with evaluations of credibility indicators (Bhuiyan et al. 2020 ; Nygren et al. 2019 ; Shao et al. 2018a ; Pennycook et al. 2020a , b ; Clayton et al. 2020 ; Ozturk et al. 2015 ; Metzger et al. 2020 ; Sherman et al. 2020 ; Nekmat 2020 ; Brashier et al. 2021 ; Chung and Kim 2021 ; Lanius et al. 2021 ); as well as social media-driven studies , which investigate the effect of signals (e.g., sources) to detect and recognize fake news (Vraga and Bode 2017 ; Jakesch et al. 2019 ; Shen et al. 2019 ; Avram et al. 2020 ; Hameleers et al. 2020 ; Dias et al. 2020 ; Nyhan et al. 2020 ; Bode and Vraga 2015 ; Tsang 2020 ; Vishwakarma et al. 2019 ; Yavary et al. 2020 ) and investigate fake and reliable news sources using complex networks analysis based on search engine optimization metric (Mazzeo and Rapisarda 2022 ).

The impacts of fake news have reached various areas and disciplines beyond online social networks and society (García et al. 2020 ) such as economics (Clarke et al. 2020 ; Kogan et al. 2019 ; Goldstein and Yang 2019 ), psychology (Roozenbeek et al. 2020a ; Van der Linden and Roozenbeek 2020 ; Roozenbeek and van der Linden 2019 ), political science (Valenzuela et al. 2022 ; Bringula et al. 2022 ; Ricard and Medeiros 2020 ; Van der Linden et al. 2020 ; Allcott and Gentzkow 2017 ; Grinberg et al. 2019 ; Guess et al. 2019 ; Baptista and Gradim 2020 ), health science (Alonso-Galbán and Alemañy-Castilla 2022 ; Desai et al. 2022 ; Apuke and Omar 2021 ; Escolà-Gascón 2021 ; Wang et al. 2019c ; Hartley and Vu 2020 ; Micallef et al. 2020 ; Pennycook et al. 2020b ; Sharma et al. 2020 ; Roozenbeek et al. 2020b ), environmental science (e.g., climate change) (Treen et al. 2020 ; Lutzke et al. 2019 ; Lewandowsky 2020 ; Maertens et al. 2020 ), etc.

Interesting research has been carried out to review and study the fake news issue in online social networks. Some focus not only on fake news, but also distinguish between fake news and rumor (Bondielli and Marcelloni 2019 ; Meel and Vishwakarma 2020 ), while others tackle the whole problem, from characterization to processing techniques (Shu et al. 2017 ; Guo et al. 2020 ; Zhou and Zafarani 2020 ). However, they mostly focus on studying approaches from a machine learning perspective (Bondielli and Marcelloni 2019 ), data mining perspective (Shu et al. 2017 ), crowd intelligence perspective (Guo et al. 2020 ), or knowledge-based perspective (Zhou and Zafarani 2020 ). Furthermore, most of these studies ignore at least one of the mentioned perspectives, and in many cases, they do not cover other existing detection approaches using methods such as blockchain and fact-checking, as well as analysis on metrics used for Search Engine Optimization (Mazzeo and Rapisarda 2022 ). However, in our work and to the best of our knowledge, we cover all the approaches used for fake news detection. Indeed, we investigate the proposed solutions from broader perspectives (i.e., the detection techniques that are used, as well as the different aspects and types of the information used).

Therefore, in this paper, we are highly motivated by the following facts. First, fake news detection on social media is still in the early age of development, and many challenging issues remain that require deeper investigation. Hence, it is necessary to discuss potential research directions that can improve fake news detection and mitigation tasks. However, the dynamic nature of fake news propagation through social networks further complicates matters (Sharma et al. 2019 ). False information can easily reach and impact a large number of users in a short time (Friggeri et al. 2014 ; Qian et al. 2018 ). Moreover, fact-checking organizations cannot keep up with the dynamics of propagation as they require human verification, which can hold back a timely and cost-effective response (Kim et al. 2018 ; Ruchansky et al. 2017 ; Shu et al. 2018a ).

Our work focuses primarily on understanding the “fake news” problem, its related challenges and root causes, and reviewing automatic fake news detection and mitigation methods in online social networks as addressed by researchers. The main contributions that differentiate us from other works are summarized below:

• We present the general context from which the fake news problem emerged (i.e., online deception)
• We review existing definitions of fake news, identify the terms and features most commonly used to define fake news, and categorize related works accordingly.
• We propose a fake news typology classification based on the various categorizations of fake news reported in the literature.
• We point out the most challenging factors preventing researchers from proposing highly effective solutions for automatic fake news detection in social media.
• We highlight and classify representative studies in the domain of automatic fake news detection and mitigation on online social networks including the key methods and techniques used to generate detection models.
• We discuss the key shortcomings that may inhibit the effectiveness of the proposed fake news detection methods in online social networks.
• We provide recommendations that can help address these shortcomings and improve the quality of research in this domain.

The rest of this article is organized as follows. We explain the methodology with which the studied references are collected and selected in Sect.  2 . We introduce the online deception problem in Sect.  3 . We highlight the modern-day problem of fake news in Sect.  4 , followed by challenges facing fake news detection and mitigation tasks in Sect.  5 . We provide a comprehensive literature review of the most relevant scholarly works on fake news detection in Sect.  6 . We provide a critical discussion and recommendations that may fill some of the gaps we have identified, as well as a classification of the reviewed automatic fake news detection approaches, in Sect.  7 . Finally, we provide a conclusion and propose some future directions in Sect.  8 .

Review methodology

This section introduces the systematic review methodology on which we relied to perform our study. We start with the formulation of the research questions, which allowed us to select the relevant research literature. Then, we provide the different sources of information together with the search and inclusion/exclusion criteria we used to select the final set of papers.

Research questions formulation

The research scope, research questions, and inclusion/exclusion criteria were established following an initial evaluation of the literature and the following research questions were formulated and addressed.

• RQ1: what is fake news in social media, how is it defined in the literature, what are its related concepts, and the different types of it?
• RQ2: What are the existing challenges and issues related to fake news?
• RQ3: What are the available techniques used to perform fake news detection in social media?

Sources of information

We broadly searched for journal and conference research articles, books, and magazines as a source of data to extract relevant articles. We used the main sources of scientific databases and digital libraries in our search, such as Google Scholar, 19 IEEE Xplore, 20 Springer Link, 21 ScienceDirect, 22 Scopus, 23 ACM Digital Library. 24 Also, we screened most of the related high-profile conferences such as WWW, SIGKDD, VLDB, ICDE and so on to find out the recent work.

Search criteria

We focused our research over a period of ten years, but we made sure that about two-thirds of the research papers that we considered were published in or after 2019. Additionally, we defined a set of keywords to search the above-mentioned scientific databases since we concentrated on reviewing the current state of the art in addition to the challenges and the future direction. The set of keywords includes the following terms: fake news, disinformation, misinformation, information disorder, social media, detection techniques, detection methods, survey, literature review.

Study selection, exclusion and inclusion criteria

To retrieve relevant research articles, based on our sources of information and search criteria, a systematic keyword-based search was carried out by posing different search queries, as shown in Table  1 .

List of keywords for searching relevant articles

We discovered a primary list of articles. On the obtained initial list of studies, we applied a set of inclusion/exclusion criteria presented in Table  2 to select the appropriate research papers. The inclusion and exclusion principles are applied to determine whether a study should be included or not.

Inclusion and exclusion criteria

After reading the abstract, we excluded some articles that did not meet our criteria. We chose the most important research to help us understand the field. We reviewed the articles completely and found only 61 research papers that discuss the definition of the term fake news and its related concepts (see Table  4 ). We used the remaining papers to understand the field, reveal the challenges, review the detection techniques, and discuss future directions.

Classification of fake news definitions based on the used term and features

A brief introduction of online deception

The Cambridge Online Dictionary defines Deception as “ the act of hiding the truth, especially to get an advantage .” Deception relies on peoples’ trust, doubt and strong emotions that may prevent them from thinking and acting clearly (Aïmeur et al. 2018 ). We also define it in previous work (Aïmeur et al. 2018 ) as the process that undermines the ability to consciously make decisions and take convenient actions, following personal values and boundaries. In other words, deception gets people to do things they would not otherwise do. In the context of online deception, several factors need to be considered: the deceiver, the purpose or aim of the deception, the social media service, the deception technique and the potential target (Aïmeur et al. 2018 ; Hage et al. 2021 ).

Researchers are working on developing new ways to protect users and prevent online deception (Aïmeur et al. 2018 ). Due to the sophistication of attacks, this is a complex task. Hence, malicious attackers are using more complex tools and strategies to deceive users. Furthermore, the way information is organized and exchanged in social media may lead to exposing OSN users to many risks (Aïmeur et al. 2013 ).

In fact, this field is one of the recent research areas that need collaborative efforts of multidisciplinary practices such as psychology, sociology, journalism, computer science as well as cyber-security and digital marketing (which are not yet well explored in the field of dis/mis/malinformation but relevant for future research). Moreover, Ismailov et al. ( 2020 ) analyzed the main causes that could be responsible for the efficiency gap between laboratory results and real-world implementations.

In this paper, it is not in our scope of work to review online deception state of the art. However, we think it is crucial to note that fake news, misinformation and disinformation are indeed parts of the larger landscape of online deception (Hage et al. 2021 ).

Fake news, the modern-day problem

Fake news has existed for a very long time, much before their wide circulation became facilitated by the invention of the printing press. 25 For instance, Socrates was condemned to death more than twenty-five hundred years ago under the fake news that he was guilty of impiety against the pantheon of Athens and corruption of the youth. 26 A Google Trends Analysis of the term “fake news” reveals an explosion in popularity around the time of the 2016 US presidential election. 27 Fake news detection is a problem that has recently been addressed by numerous organizations, including the European Union 28 and NATO. 29

In this section, we first overview the fake news definitions as they were provided in the literature. We identify the terms and features used in the definitions, and we classify the latter based on them. Then, we provide a fake news typology based on distinct categorizations that we propose, and we define and compare the most cited forms of one specific fake news category (i.e., the intent-based fake news category).

Definitions of fake news

“Fake news” is defined in the Collins English Dictionary as false and often sensational information disseminated under the guise of news reporting, 30 yet the term has evolved over time and has become synonymous with the spread of false information (Cooke 2017 ).

The first definition of the term fake news was provided by Allcott and Gentzkow ( 2017 ) as news articles that are intentionally and verifiably false and could mislead readers. Then, other definitions were provided in the literature, but they all agree on the authenticity of fake news to be false (i.e., being non-factual). However, they disagree on the inclusion and exclusion of some related concepts such as satire , rumors , conspiracy theories , misinformation and hoaxes from the given definition. More recently, Nakov ( 2020 ) reported that the term fake news started to mean different things to different people, and for some politicians, it even means “news that I do not like.”

Hence, there is still no agreed definition of the term “fake news.” Moreover, we can find many terms and concepts in the literature that refer to fake news (Van der Linden et al. 2020 ; Molina et al. 2021 ) (Abu Arqoub et al. 2022 ; Allen et al. 2020 ; Allcott and Gentzkow 2017 ; Shu et al. 2017 ; Sharma et al. 2019 ; Zhou and Zafarani 2020 ; Zhang and Ghorbani 2020 ; Conroy et al. 2015 ; Celliers and Hattingh 2020 ; Nakov 2020 ; Shu et al. 2020c ; Jin et al. 2016 ; Rubin et al. 2016 ; Balmas 2014 ; Brewer et al. 2013 ; Egelhofer and Lecheler 2019 ; Mustafaraj and Metaxas 2017 ; Klein and Wueller 2017 ; Potthast et al. 2017 ; Lazer et al. 2018 ; Weiss et al. 2020 ; Tandoc Jr et al. 2021 ; Guadagno and Guttieri 2021 ), disinformation (Kapantai et al. 2021 ; Shu et al. 2020a , c ; Kumar et al. 2016 ; Bhattacharjee et al. 2020 ; Marsden et al. 2020 ; Jungherr and Schroeder 2021 ; Starbird et al. 2019 ; Ireton and Posetti 2018 ), misinformation (Wu et al. 2019 ; Shu et al. 2020c ; Shao et al. 2016 , 2018b ; Pennycook and Rand 2019 ; Micallef et al. 2020 ), malinformation (Dame Adjin-Tettey 2022 ) (Carmi et al. 2020 ; Shu et al. 2020c ), false information (Kumar and Shah 2018 ; Guo et al. 2020 ; Habib et al. 2019 ), information disorder (Shu et al. 2020c ; Wardle and Derakhshan 2017 ; Wardle 2018 ; Derakhshan and Wardle 2017 ), information warfare (Guadagno and Guttieri 2021 ) and information pollution (Meel and Vishwakarma 2020 ).

There is also a remarkable amount of disagreement over the classification of the term fake news in the research literature, as well as in policy (de Cock Buning 2018 ; ERGA 2018 , 2021 ). Some consider fake news as a type of misinformation (Allen et al. 2020 ; Singh et al. 2021 ; Ha et al. 2021 ; Pennycook and Rand 2019 ; Shao et al. 2018b ; Di Domenico et al. 2021 ; Sharma et al. 2019 ; Celliers and Hattingh 2020 ; Klein and Wueller 2017 ; Potthast et al. 2017 ; Islam et al. 2020 ), others consider it as a type of disinformation (de Cock Buning 2018 ) (Bringula et al. 2022 ; Baptista and Gradim 2022 ; Tsang 2020 ; Tandoc Jr et al. 2021 ; Bastick 2021 ; Khan et al. 2019 ; Shu et al. 2017 ; Nakov 2020 ; Shu et al. 2020c ; Egelhofer and Lecheler 2019 ), while others associate the term with both disinformation and misinformation (Wu et al. 2022 ; Dame Adjin-Tettey 2022 ; Hameleers et al. 2022 ; Carmi et al. 2020 ; Allcott and Gentzkow 2017 ; Zhang and Ghorbani 2020 ; Potthast et al. 2017 ; Weiss et al. 2020 ; Tandoc Jr et al. 2021 ; Guadagno and Guttieri 2021 ). On the other hand, some prefer to differentiate fake news from both terms (ERGA 2018 ; Molina et al. 2021 ; ERGA 2021 ) (Zhou and Zafarani 2020 ; Jin et al. 2016 ; Rubin et al. 2016 ; Balmas 2014 ; Brewer et al. 2013 ).

The existing terms can be separated into two groups. The first group represents the general terms, which are information disorder , false information and fake news , each of which includes a subset of terms from the second group. The second group represents the elementary terms, which are misinformation , disinformation and malinformation . The literature agrees on the definitions of the latter group, but there is still no agreed-upon definition of the first group. In Fig.  2 , we model the relationship between the most used terms in the literature.

Modeling of the relationship between terms related to fake news

The terms most used in the literature to refer, categorize and classify fake news can be summarized and defined as shown in Table  3 , in which we capture the similarities and show the differences between the different terms based on two common key features, which are the intent and the authenticity of the news content. The intent feature refers to the intention behind the term that is used (i.e., whether or not the purpose is to mislead or cause harm), whereas the authenticity feature refers to its factual aspect. (i.e., whether the content is verifiably false or not, which we label as genuine in the second case). Some of these terms are explicitly used to refer to fake news (i.e., disinformation, misinformation and false information), while others are not (i.e., malinformation). In the comparison table, the empty dash (–) cell denotes that the classification does not apply.

A comparison between used terms based on intent and authenticity

In Fig.  3 , we identify the different features used in the literature to define fake news (i.e., intent, authenticity and knowledge). Hence, some definitions are based on two key features, which are authenticity and intent (i.e., news articles that are intentionally and verifiably false and could mislead readers). However, other definitions are based on either authenticity or intent. Other researchers categorize false information on the web and social media based on its intent and knowledge (i.e., when there is a single ground truth). In Table  4 , we classify the existing fake news definitions based on the used term and the used features . In the classification, the references in the cells refer to the research study in which a fake news definition was provided, while the empty dash (–) cells denote that the classification does not apply.

The features used for fake news definition

Fake news typology

Various categorizations of fake news have been provided in the literature. We can distinguish two major categories of fake news based on the studied perspective (i.e., intention or content) as shown in Fig.  4 . However, our proposed fake news typology is not about detection methods, and it is not exclusive. Hence, a given category of fake news can be described based on both perspectives (i.e., intention and content) at the same time. For instance, satire (i.e., intent-based fake news) can contain text and/or multimedia content types of data (e.g., headline, body, image, video) (i.e., content-based fake news) and so on.

Most researchers classify fake news based on the intent (Collins et al. 2020 ; Bondielli and Marcelloni 2019 ; Zannettou et al. 2019 ; Kumar et al. 2016 ; Wardle 2017 ; Shu et al. 2017 ; Kumar and Shah 2018 ) (see Sect.  4.2.2 ). However, other researchers (Parikh and Atrey 2018 ; Fraga-Lamas and Fernández-Caramés 2020 ; Hasan and Salah 2019 ; Masciari et al. 2020 ; Bakdash et al. 2018 ; Elhadad et al. 2019 ; Yang et al. 2019b ) focus on the content to categorize types of fake news through distinguishing the different formats and content types of data in the news (e.g., text and/or multimedia).

Recently, another classification was proposed by Zhang and Ghorbani ( 2020 ). It is based on the combination of content and intent to categorize fake news. They distinguish physical news content and non-physical news content from fake news. Physical content consists of the carriers and format of the news, and non-physical content consists of the opinions, emotions, attitudes and sentiments that the news creators want to express.

Content-based fake news category

According to researchers of this category (Parikh and Atrey 2018 ; Fraga-Lamas and Fernández-Caramés 2020 ; Hasan and Salah 2019 ; Masciari et al. 2020 ; Bakdash et al. 2018 ; Elhadad et al. 2019 ; Yang et al. 2019b ), forms of fake news may include false text such as hyperlinks or embedded content; multimedia such as false videos (Demuyakor and Opata 2022 ), images (Masciari et al. 2020 ; Shen et al. 2019 ), audios (Demuyakor and Opata 2022 ) and so on. Moreover, we can also find multimodal content (Shu et al. 2020a ) that is fake news articles and posts composed of multiple types of data combined together, for example, a fabricated image along with a text related to the image (Shu et al. 2020a ). In this category of fake news forms, we can mention as examples deepfake videos (Yang et al. 2019b ) and GAN-generated fake images (Zhang et al. 2019b ), which are artificial intelligence-based machine-generated fake content that are hard for unsophisticated social network users to identify.

The effects of these forms of fake news content vary on the credibility assessment, as well as sharing intentions which influences the spread of fake news on OSNs. For instance, people with little knowledge about the issue compared to those who are strongly concerned about the key issue of fake news tend to be easier to convince that the misleading or fake news is real, especially when shared via a video modality as compared to the text or the audio modality (Demuyakor and Opata 2022 ).

Intent-based Fake News Category

The most often mentioned and discussed forms of fake news according to researchers in this category include but are not restricted to clickbait , hoax , rumor , satire , propaganda , framing , conspiracy theories and others. In the following subsections, we explain these types of fake news as they were defined in the literature and undertake a brief comparison between them as depicted in Table  5 . The following are the most cited forms of intent-based types of fake news, and their comparison is based on what we suspect are the most common criteria mentioned by researchers.

A comparison between the different types of intent-based fake news

Clickbait refers to misleading headlines and thumbnails of content on the web (Zannettou et al. 2019 ) that tend to be fake stories with catchy headlines aimed at enticing the reader to click on a link (Collins et al. 2020 ). This type of fake news is considered to be the least severe type of false information because if a user reads/views the whole content, it is possible to distinguish if the headline and/or the thumbnail was misleading (Zannettou et al. 2019 ). However, the goal behind using clickbait is to increase the traffic to a website (Zannettou et al. 2019 ).

A hoax is a false (Zubiaga et al. 2018 ) or inaccurate (Zannettou et al. 2019 ) intentionally fabricated (Collins et al. 2020 ) news story used to masquerade the truth (Zubiaga et al. 2018 ) and is presented as factual (Zannettou et al. 2019 ) to deceive the public or audiences (Collins et al. 2020 ). This category is also known either as half-truth or factoid stories (Zannettou et al. 2019 ). Popular examples of hoaxes are stories that report the false death of celebrities (Zannettou et al. 2019 ) and public figures (Collins et al. 2020 ). Recently, hoaxes about the COVID-19 have been circulating through social media.

The term rumor refers to ambiguous or never confirmed claims (Zannettou et al. 2019 ) that are disseminated with a lack of evidence to support them (Sharma et al. 2019 ). This kind of information is widely propagated on OSNs (Zannettou et al. 2019 ). However, they are not necessarily false and may turn out to be true (Zubiaga et al. 2018 ). Rumors originate from unverified sources but may be true or false or remain unresolved (Zubiaga et al. 2018 ).

Satire refers to stories that contain a lot of irony and humor (Zannettou et al. 2019 ). It presents stories as news that might be factually incorrect, but the intent is not to deceive but rather to call out, ridicule, or to expose behavior that is shameful, corrupt, or otherwise “bad” (Golbeck et al. 2018 ). This is done with a fabricated story or by exaggerating the truth reported in mainstream media in the form of comedy (Collins et al. 2020 ). The intent behind satire seems kind of legitimate and many authors (such as Wardle (Wardle 2017 )) do include satire as a type of fake news as there is no intention to cause harm but it has the potential to mislead or fool people.

Also, Golbeck et al. ( 2018 ) mention that there is a spectrum from fake to satirical news that they found to be exploited by many fake news sites. These sites used disclaimers at the bottom of their webpages to suggest they were “satirical” even when there was nothing satirical about their articles, to protect them from accusations about being fake. The difference with a satirical form of fake news is that the authors or the host present themselves as a comedian or as an entertainer rather than a journalist informing the public (Collins et al. 2020 ). However, most audiences believed the information passed in this satirical form because the comedian usually projects news from mainstream media and frames them to suit their program (Collins et al. 2020 ).

Propaganda refers to news stories created by political entities to mislead people. It is a special instance of fabricated stories that aim to harm the interests of a particular party and, typically, has a political context (Zannettou et al. 2019 ). Propaganda was widely used during both World Wars (Collins et al. 2020 ) and during the Cold War (Zannettou et al. 2019 ). It is a consequential type of false information as it can change the course of human history (e.g., by changing the outcome of an election) (Zannettou et al. 2019 ). States are the main actors of propaganda. Recently, propaganda has been used by politicians and media organizations to support a certain position or view (Collins et al. 2020 ). Online astroturfing can be an example of the tools used for the dissemination of propaganda. It is a covert manipulation of public opinion (Peng et al. 2017 ) that aims to make it seem that many people share the same opinion about something. Astroturfing can affect different domains of interest, based on which online astroturfing can be mainly divided into political astroturfing, corporate astroturfing and astroturfing in e-commerce or online services (Mahbub et al. 2019 ). Propaganda types of fake news can be debunked with manual fact-based detection models such as the use of expert-based fact-checkers (Collins et al. 2020 ).

Framing refers to employing some aspect of reality to make content more visible, while the truth is concealed (Collins et al. 2020 ) to deceive and misguide readers. People will understand certain concepts based on the way they are coined and invented. An example of framing was provided by Collins et al. ( 2020 ): “suppose a leader X says “I will neutralize my opponent” simply meaning he will beat his opponent in a given election. Such a statement will be framed such as “leader X threatens to kill Y” and this framed statement provides a total misrepresentation of the original meaning.

Conspiracy Theories

Conspiracy theories refer to the belief that an event is the result of secret plots generated by powerful conspirators. Conspiracy belief refers to people’s adoption and belief of conspiracy theories, and it is associated with psychological, political and social factors (Douglas et al. 2019 ). Conspiracy theories are widespread in contemporary democracies (Sutton and Douglas 2020 ), and they have major consequences. For instance, lately and during the COVID-19 pandemic, conspiracy theories have been discussed from a public health perspective (Meese et al. 2020 ; Allington et al. 2020 ; Freeman et al. 2020 ).

Comparison Between Most Popular Intent-based Types of Fake News

Following a review of the most popular intent-based types of fake news, we compare them as shown in Table  5 based on the most common criteria mentioned by researchers in their definitions as listed below.

• the intent behind the news, which refers to whether a given news type was mainly created to intentionally deceive people or not (e.g., humor, irony, entertainment, etc.);
• the way that the news propagates through OSN, which determines the nature of the propagation of each type of fake news and this can be either fast or slow propagation;
• the severity of the impact of the news on OSN users, which refers to whether the public has been highly impacted by the given type of fake news; the mentioned impact of each fake news type is mainly the proportion of the negative impact;
• and the goal behind disseminating the news, which can be to gain popularity for a particular entity (e.g., political party), for profit (e.g., lucrative business), or other reasons such as humor and irony in the case of satire, spreading panic or anger, and manipulating the public in the case of hoaxes, made-up stories about a particular person or entity in the case of rumors, and misguiding readers in the case of framing.

However, the comparison provided in Table  5 is deduced from the studied research papers; it is our point of view, which is not based on empirical data.

We suspect that the most dangerous types of fake news are the ones with high intention to deceive the public, fast propagation through social media, high negative impact on OSN users, and complicated hidden goals and agendas. However, while the other types of fake news are less dangerous, they should not be ignored.

Moreover, it is important to highlight that the existence of the overlap in the types of fake news mentioned above has been proven, thus it is possible to observe false information that may fall within multiple categories (Zannettou et al. 2019 ). Here, we provide two examples by Zannettou et al. ( 2019 ) to better understand possible overlaps: (1) a rumor may also use clickbait techniques to increase the audience that will read the story; and (2) propaganda stories, as a special instance of a framing story.

Challenges related to fake news detection and mitigation

To alleviate fake news and its threats, it is crucial to first identify and understand the factors involved that continue to challenge researchers. Thus, the main question is to explore and investigate the factors that make it easier to fall for manipulated information. Despite the tremendous progress made in alleviating some of the challenges in fake news detection (Sharma et al. 2019 ; Zhou and Zafarani 2020 ; Zhang and Ghorbani 2020 ; Shu et al. 2020a ), much more work needs to be accomplished to address the problem effectively.

In this section, we discuss several open issues that have been making fake news detection in social media a challenging problem. These issues can be summarized as follows: content-based issues (i.e., deceptive content that resembles the truth very closely), contextual issues (i.e., lack of user awareness, social bots spreaders of fake content, and OSN’s dynamic natures that leads to the fast propagation), as well as the issue of existing datasets (i.e., there still no one size fits all benchmark dataset for fake news detection). These various aspects have proven (Shu et al. 2017 ) to have a great impact on the accuracy of fake news detection approaches.

Content-based issue, deceptive content

Automatic fake news detection remains a huge challenge, primarily because the content is designed in a way that it closely resembles the truth. Besides, most deceivers choose their words carefully and use their language strategically to avoid being caught. Therefore, it is often hard to determine its veracity by AI without the reliance on additional information from third parties such as fact-checkers.

Abdullah-All-Tanvir et al. ( 2020 ) reported that fake news tends to have more complicated stories and hardly ever make any references. It is more likely to contain a greater number of words that express negative emotions. This makes it so complicated that it becomes impossible for a human to manually detect the credibility of this content. Therefore, detecting fake news on social media is quite challenging. Moreover, fake news appears in multiple types and forms, which makes it hard and challenging to define a single global solution able to capture and deal with the disseminated content. Consequently, detecting false information is not a straightforward task due to its various types and forms Zannettou et al. ( 2019 ).

Contextual issues

Contextual issues are challenges that we suspect may not be related to the content of the news but rather they are inferred from the context of the online news post (i.e., humans are the weakest factor due to lack of user awareness, social bots spreaders, dynamic nature of online social platforms and fast propagation of fake news).

Humans are the weakest factor due to the lack of awareness

Recent statistics 31 show that the percentage of unintentional fake news spreaders (people who share fake news without the intention to mislead) over social media is five times higher than intentional spreaders. Moreover, another recent statistic 32 shows that the percentage of people who were confident about their ability to discern fact from fiction is ten times higher than those who were not confident about the truthfulness of what they are sharing. As a result, we can deduce the lack of human awareness about the ascent of fake news.

Public susceptibility and lack of user awareness (Sharma et al. 2019 ) have always been the most challenging problem when dealing with fake news and misinformation. This is a complex issue because many people believe almost everything on the Internet and the ones who are new to digital technology or have less expertise may be easily fooled (Edgerly et al. 2020 ).

Moreover, it has been widely proven (Metzger et al. 2020 ; Edgerly et al. 2020 ) that people are often motivated to support and accept information that goes with their preexisting viewpoints and beliefs, and reject information that does not fit in as well. Hence, Shu et al. ( 2017 ) illustrate an interesting correlation between fake news spread and psychological and cognitive theories. They further suggest that humans are more likely to believe information that confirms their existing views and ideological beliefs. Consequently, they deduce that humans are naturally not very good at differentiating real information from fake information.

Recent research by Giachanou et al. ( 2020 ) studies the role of personality and linguistic patterns in discriminating between fake news spreaders and fact-checkers. They classify a user as a potential fact-checker or a potential fake news spreader based on features that represent users’ personality traits and linguistic patterns used in their tweets. They show that leveraging personality traits and linguistic patterns can improve the performance in differentiating between checkers and spreaders.

Furthermore, several researchers studied the prevalence of fake news on social networks during (Allcott and Gentzkow 2017 ; Grinberg et al. 2019 ; Guess et al. 2019 ; Baptista and Gradim 2020 ) and after (Garrett and Bond 2021 ) the 2016 US presidential election and found that individuals most likely to engage with fake news sources were generally conservative-leaning, older, and highly engaged with political news.

Metzger et al. ( 2020 ) examine how individuals evaluate the credibility of biased news sources and stories. They investigate the role of both cognitive dissonance and credibility perceptions in selective exposure to attitude-consistent news information. They found that online news consumers tend to perceive attitude-consistent news stories as more accurate and more credible than attitude-inconsistent stories.

Similarly, Edgerly et al. ( 2020 ) explore the impact of news headlines on the audience’s intent to verify whether given news is true or false. They concluded that participants exhibit higher intent to verify the news only when they believe the headline to be true, which is predicted by perceived congruence with preexisting ideological tendencies.

Luo et al. ( 2022 ) evaluate the effects of endorsement cues in social media on message credibility and detection accuracy. Results showed that headlines associated with a high number of likes increased credibility, thereby enhancing detection accuracy for real news but undermining accuracy for fake news. Consequently, they highlight the urgency of empowering individuals to assess both news veracity and endorsement cues appropriately on social media.

Moreover, misinformed people are a greater problem than uninformed people (Kuklinski et al. 2000 ), because the former hold inaccurate opinions (which may concern politics, climate change, medicine) that are harder to correct. Indeed, people find it difficult to update their misinformation-based beliefs even after they have been proved to be false (Flynn et al. 2017 ). Moreover, even if a person has accepted the corrected information, his/her belief may still affect their opinion (Nyhan and Reifler 2015 ).

Falling for disinformation may also be explained by a lack of critical thinking and of the need for evidence that supports information (Vilmer et al. 2018 ; Badawy et al. 2019 ). However, it is also possible that people choose misinformation because they engage in directionally motivated reasoning (Badawy et al. 2019 ; Flynn et al. 2017 ). Online clients are normally vulnerable and will, in general, perceive web-based networking media as reliable, as reported by Abdullah-All-Tanvir et al. ( 2019 ), who propose to mechanize fake news recognition.

It is worth noting that in addition to bots causing the outpouring of the majority of the misrepresentations, specific individuals are also contributing a large share of this issue (Abdullah-All-Tanvir et al. 2019 ). Furthermore, Vosoughi et al. (Vosoughi et al. 2018 ) found that contrary to conventional wisdom, robots have accelerated the spread of real and fake news at the same rate, implying that fake news spreads more than the truth because humans, not robots, are more likely to spread it.

In this case, verified users and those with numerous followers were not necessarily responsible for spreading misinformation of the corrupted posts (Abdullah-All-Tanvir et al. 2019 ).

Viral fake news can cause much havoc to our society. Therefore, to mitigate the negative impact of fake news, it is important to analyze the factors that lead people to fall for misinformation and to further understand why people spread fake news (Cheng et al. 2020 ). Measuring the accuracy, credibility, veracity and validity of news contents can also be a key countermeasure to consider.

Social bots spreaders

Several authors (Shu et al. 2018b , 2017 ; Shi et al. 2019 ; Bessi and Ferrara 2016 ; Shao et al. 2018a ) have also shown that fake news is likely to be created and spread by non-human accounts with similar attributes and structure in the network, such as social bots (Ferrara et al. 2016 ). Bots (short for software robots) exist since the early days of computers. A social bot is a computer algorithm that automatically produces content and interacts with humans on social media, trying to emulate and possibly alter their behavior (Ferrara et al. 2016 ). Although they are designed to provide a useful service, they can be harmful, for example when they contribute to the spread of unverified information or rumors (Ferrara et al. 2016 ). However, it is important to note that bots are simply tools created and maintained by humans for some specific hidden agendas.

Social bots tend to connect with legitimate users instead of other bots. They try to act like a human with fewer words and fewer followers on social media. This contributes to the forwarding of fake news (Jiang et al. 2019 ). Moreover, there is a difference between bot-generated and human-written clickbait (Le et al. 2019 ).

Many researchers have addressed ways of identifying and analyzing possible sources of fake news spread in social media. Recent research by Shu et al. ( 2020a ) describes social bots use of two strategies to spread low-credibility content. First, they amplify interactions with content as soon as it is created to make it look legitimate and to facilitate its spread across social networks. Next, they try to increase public exposure to the created content and thus boost its perceived credibility by targeting influential users that are more likely to believe disinformation in the hope of getting them to “repost” the fabricated content. They further discuss the social bot detection systems taxonomy proposed by Ferrara et al. ( 2016 ) which divides bot detection methods into three classes: (1) graph-based, (2) crowdsourcing and (3) feature-based social bot detection methods.

Similarly, Shao et al. ( 2018a ) examine social bots and how they promote the spread of misinformation through millions of Twitter posts during and following the 2016 US presidential campaign. They found that social bots played a disproportionate role in spreading articles from low-credibility sources by amplifying such content in the early spreading moments and targeting users with many followers through replies and mentions to expose them to this content and induce them to share it.

Ismailov et al. ( 2020 ) assert that the techniques used to detect bots depend on the social platform and the objective. They note that a malicious bot designed to make friends with as many accounts as possible will require a different detection approach than a bot designed to repeatedly post links to malicious websites. Therefore, they identify two models for detecting malicious accounts, each using a different set of features. Social context models achieve detection by examining features related to an account’s social presence including features such as relationships to other accounts, similarities to other users’ behaviors, and a variety of graph-based features. User behavior models primarily focus on features related to an individual user’s behavior, such as frequency of activities (e.g., number of tweets or posts per time interval), patterns of activity and clickstream sequences.

Therefore, it is crucial to consider bot detection techniques to distinguish bots from normal users to better leverage user profile features to detect fake news.

However, there is also another “bot-like” strategy that aims to massively promote disinformation and fake content in social platforms, which is called bot farms or also troll farms. It is not social bots, but it is a group of organized individuals engaging in trolling or bot-like promotion of narratives in a coordinated fashion (Wardle 2018 ) hired to massively spread fake news or any other harmful content. A prominent troll farm example is the Russia-based Internet Research Agency (IRA), which disseminated inflammatory content online to influence the outcome of the 2016 U.S. presidential election. 33 As a result, Twitter suspended accounts connected to the IRA and deleted 200,000 tweets from Russian trolls (Jamieson 2020 ). Another example to mention in this category is review bombing (Moro and Birt 2022 ). Review bombing refers to coordinated groups of people massively performing the same negative actions online (e.g., dislike, negative review/comment) on an online video, game, post, product, etc., in order to reduce its aggregate review score. The review bombers can be both humans and bots coordinated in order to cause harm and mislead people by falsifying facts.

Dynamic nature of online social platforms and fast propagation of fake news

Sharma et al. ( 2019 ) affirm that the fast proliferation of fake news through social networks makes it hard and challenging to assess the information’s credibility on social media. Similarly, Qian et al. ( 2018 ) assert that fake news and fabricated content propagate exponentially at the early stage of its creation and can cause a significant loss in a short amount of time (Friggeri et al. 2014 ) including manipulating the outcome of political events (Liu and Wu 2018 ; Bessi and Ferrara 2016 ).

Moreover, while analyzing the way source and promoters of fake news operate over the web through multiple online platforms, Zannettou et al. ( 2019 ) discovered that false information is more likely to spread across platforms (18% appearing on multiple platforms) compared to real information (11%).

Furthermore, recently, Shu et al. ( 2020c ) attempted to understand the propagation of disinformation and fake news in social media and found that such content is produced and disseminated faster and easier through social media because of the low barriers that prevent doing so. Similarly, Shu et al. ( 2020b ) studied hierarchical propagation networks for fake news detection. They performed a comparative analysis between fake and real news from structural, temporal and linguistic perspectives. They demonstrated the potential of using these features to detect fake news and they showed their effectiveness for fake news detection as well.

Lastly, Abdullah-All-Tanvir et al. ( 2020 ) note that it is almost impossible to manually detect the sources and authenticity of fake news effectively and efficiently, due to its fast circulation in such a small amount of time. Therefore, it is crucial to note that the dynamic nature of the various online social platforms, which results in the continued rapid and exponential propagation of such fake content, remains a major challenge that requires further investigation while defining innovative solutions for fake news detection.

Datasets issue

The existing approaches lack an inclusive dataset with derived multidimensional information to detect fake news characteristics to achieve higher accuracy of machine learning classification model performance (Nyow and Chua 2019 ). These datasets are primarily dedicated to validating the machine learning model and are the ultimate frame of reference to train the model and analyze its performance. Therefore, if a researcher evaluates their model based on an unrepresentative dataset, the validity and the efficiency of the model become questionable when it comes to applying the fake news detection approach in a real-world scenario.

Moreover, several researchers (Shu et al. 2020d ; Wang et al. 2020 ; Pathak and Srihari 2019 ; Przybyla 2020 ) believe that fake news is diverse and dynamic in terms of content, topics, publishing methods and media platforms, and sophisticated linguistic styles geared to emulate true news. Consequently, training machine learning models on such sophisticated content requires large-scale annotated fake news data that are difficult to obtain (Shu et al. 2020d ).

Therefore, datasets are also a great topic to work on to enhance data quality and have better results while defining our solutions. Adversarial learning techniques (e.g., GAN, SeqGAN) can be used to provide machine-generated data that can be used to train deeper models and build robust systems to detect fake examples from the real ones. This approach can be used to counter the lack of datasets and the scarcity of data available to train models.

Fake news detection literature review

Fake news detection in social networks is still in the early stage of development and there are still challenging issues that need further investigation. This has become an emerging research area that is attracting huge attention.

There are various research studies on fake news detection in online social networks. Few of them have focused on the automatic detection of fake news using artificial intelligence techniques. In this section, we review the existing approaches used in automatic fake news detection, as well as the techniques that have been adopted. Then, a critical discussion built on a primary classification scheme based on a specific set of criteria is also emphasized.

Categories of fake news detection

In this section, we give an overview of most of the existing automatic fake news detection solutions adopted in the literature. A recent classification by Sharma et al. ( 2019 ) uses three categories of fake news identification methods. Each category is further divided based on the type of existing methods (i.e., content-based, feedback-based and intervention-based methods). However, a review of the literature for fake news detection in online social networks shows that the existing studies can be classified into broader categories based on two major aspects that most authors inspect and make use of to define an adequate solution. These aspects can be considered as major sources of extracted information used for fake news detection and can be summarized as follows: the content-based (i.e., related to the content of the news post) and the contextual aspect (i.e., related to the context of the news post).

Consequently, the studies we reviewed can be classified into three different categories based on the two aspects mentioned above (the third category is hybrid). As depicted in Fig.  5 , fake news detection solutions can be categorized as news content-based approaches, the social context-based approaches that can be divided into network and user-based approaches, and hybrid approaches. The latter combines both content-based and contextual approaches to define the solution.

Classification of fake news detection approaches

News Content-based Category

News content-based approaches are fake news detection approaches that use content information (i.e., information extracted from the content of the news post) and that focus on studying and exploiting the news content in their proposed solutions. Content refers to the body of the news, including source, headline, text and image-video, which can reflect subtle differences.

Researchers of this category rely on content-based detection cues (i.e., text and multimedia-based cues), which are features extracted from the content of the news post. Text-based cues are features extracted from the text of the news, whereas multimedia-based cues are features extracted from the images and videos attached to the news. Figure  6 summarizes the most widely used news content representation (i.e., text and multimedia/images) and detection techniques (i.e., machine learning (ML), deep Learning (DL), natural language processing (NLP), fact-checking, crowdsourcing (CDS) and blockchain (BKC)) in news content-based category of fake news detection approaches. Most of the reviewed research works based on news content for fake news detection rely on the text-based cues (Kapusta et al. 2019 ; Kaur et al. 2020 ; Vereshchaka et al. 2020 ; Ozbay and Alatas 2020 ; Wang 2017 ; Nyow and Chua 2019 ; Hosseinimotlagh and Papalexakis 2018 ; Abdullah-All-Tanvir et al. 2019 , 2020 ; Mahabub 2020 ; Bahad et al. 2019 ; Hiriyannaiah et al. 2020 ) extracted from the text of the news content including the body of the news and its headline. However, a few researchers such as Vishwakarma et al. ( 2019 ) and Amri et al. ( 2022 ) try to recognize text from the associated image.

News content-based category: news content representation and detection techniques

Most researchers of this category rely on artificial intelligence (AI) techniques (such as ML, DL and NLP models) to improve performance in terms of prediction accuracy. Others use different techniques such as fact-checking, crowdsourcing and blockchain. Specifically, the AI- and ML-based approaches in this category are trying to extract features from the news content, which they use later for content analysis and training tasks. In this particular case, the extracted features are the different types of information considered to be relevant for the analysis. Feature extraction is considered as one of the best techniques to reduce data size in automatic fake news detection. This technique aims to choose a subset of features from the original set to improve classification performance (Yazdi et al. 2020 ).

Table  6 lists the distinct features and metadata, as well as the used datasets in the news content-based category of fake news detection approaches.

The features and datasets used in the news content-based approaches

a https://www.kaggle.com/anthonyc1/gathering-real-news-for-oct-dec-2016 , last access date: 26-12-2022

b https://mediabiasfactcheck.com/ , last access date: 26-12-2022

c https://github.com/KaiDMML/FakeNewsNet , last access date: 26-12-2022

d https://www.kaggle.com/anthonyc1/gathering-real-news-for-oct-dec-2016 , last access date: 26-12-2022

e https://www.cs.ucsb.edu/~william/data/liar_dataset.zip , last access date: 26-12-2022

f https://www.kaggle.com/mrisdal/fake-news , last access date: 26-12-2022

g https://github.com/BuzzFeedNews/2016-10-facebook-fact-check , last access date: 26-12-2022

h https://www.politifact.com/subjects/fake-news/ , last access date: 26-12-2022

i https://www.kaggle.com/rchitic17/real-or-fake , last access date: 26-12-2022

j https://www.kaggle.com/jruvika/fake-news-detection , last access date: 26-12-2022

k https://github.com/MKLab-ITI/image-verification-corpus , last access date: 26-12-2022

l https://drive.google.com/file/d/14VQ7EWPiFeGzxp3XC2DeEHi-BEisDINn/view , last access date: 26-12-2022

Social Context-based Category

Unlike news content-based solutions, the social context-based approaches capture the skeptical social context of the online news (Zhang and Ghorbani 2020 ) rather than focusing on the news content. The social context-based category contains fake news detection approaches that use the contextual aspects (i.e., information related to the context of the news post). These aspects are based on social context and they offer additional information to help detect fake news. They are the surrounding data outside of the fake news article itself, where they can be an essential part of automatic fake news detection. Some useful examples of contextual information may include checking if the news itself and the source that published it are credible, checking the date of the news or the supporting resources, and checking if any other online news platforms are reporting the same or similar stories (Zhang and Ghorbani 2020 ).

Social context-based aspects can be classified into two subcategories, user-based and network-based, and they can be used for context analysis and training tasks in the case of AI- and ML-based approaches. User-based aspects refer to information captured from OSN users such as user profile information (Shu et al. 2019b ; Wang et al. 2019c ; Hamdi et al. 2020 ; Nyow and Chua 2019 ; Jiang et al. 2019 ) and user behavior (Cardaioli et al. 2020 ) such as user engagement (Uppada et al. 2022 ; Jiang et al. 2019 ; Shu et al. 2018b ; Nyow and Chua 2019 ) and response (Zhang et al. 2019a ; Qian et al. 2018 ). Meanwhile, network-based aspects refer to information captured from the properties of the social network where the fake content is shared and disseminated such as news propagation path (Liu and Wu 2018 ; Wu and Liu 2018 ) (e.g., propagation times and temporal characteristics of propagation), diffusion patterns (Shu et al. 2019a ) (e.g., number of retweets, shares), as well as user relationships (Mishra 2020 ; Hamdi et al. 2020 ; Jiang et al. 2019 ) (e.g., friendship status among users).

Figure  7 summarizes some of the most widely adopted social context representations, as well as the most used detection techniques (i.e., AI, ML, DL, fact-checking and blockchain), in the social context-based category of approaches.

Social context-based category: social context representation and detection techniques

Table  7 lists the distinct features and metadata, the adopted detection cues, as well as the used datasets, in the context-based category of fake news detection approaches.

The features, detection cues and datasets used int the social context-based approaches

a https://www.dropbox.com/s/7ewzdrbelpmrnxu/rumdetect2017.zip , last access date: 26-12-2022 b https://snap.stanford.edu/data/ego-Twitter.html , last access date: 26-12-2022

Hybrid approaches

Most researchers are focusing on employing a specific method rather than a combination of both content- and context-based methods. This is because some of them (Wu and Rao 2020 ) believe that there still some challenging limitations in the traditional fusion strategies due to existing feature correlations and semantic conflicts. For this reason, some researchers focus on extracting content-based information, while others are capturing some social context-based information for their proposed approaches.

However, it has proven challenging to successfully automate fake news detection based on just a single type of feature (Ruchansky et al. 2017 ). Therefore, recent directions tend to do a mixture by using both news content-based and social context-based approaches for fake news detection.

Table  8 lists the distinct features and metadata, as well as the used datasets, in the hybrid category of fake news detection approaches.

The features and datasets used in the hybrid approaches

Fake news detection techniques

Another vision for classifying automatic fake news detection is to look at techniques used in the literature. Hence, we classify the detection methods based on the techniques into three groups:

• Human-based techniques: This category mainly includes the use of crowdsourcing and fact-checking techniques, which rely on human knowledge to check and validate the veracity of news content.
• Artificial Intelligence-based techniques: This category includes the most used AI approaches for fake news detection in the literature. Specifically, these are the approaches in which researchers use classical ML, deep learning techniques such as convolutional neural network (CNN), recurrent neural network (RNN), as well as natural language processing (NLP).
• Blockchain-based techniques: This category includes solutions using blockchain technology to detect and mitigate fake news in social media by checking source reliability and establishing the traceability of the news content.

Human-based Techniques

One specific research direction for fake news detection consists of using human-based techniques such as crowdsourcing (Pennycook and Rand 2019 ; Micallef et al. 2020 ) and fact-checking (Vlachos and Riedel 2014 ; Chung and Kim 2021 ; Nyhan et al. 2020 ) techniques.

These approaches can be considered as low computational requirement techniques since both rely on human knowledge and expertise for fake news detection. However, fake news identification cannot be addressed solely through human force since it demands a lot of effort in terms of time and cost, and it is ineffective in terms of preventing the fast spread of fake content.

Crowdsourcing. Crowdsourcing approaches (Kim et al. 2018 ) are based on the “wisdom of the crowds” (Collins et al. 2020 ) for fake content detection. These approaches rely on the collective contributions and crowd signals (Tschiatschek et al. 2018 ) of a group of people for the aggregation of crowd intelligence to detect fake news (Tchakounté et al. 2020 ) and to reduce the spread of misinformation on social media (Pennycook and Rand 2019 ; Micallef et al. 2020 ).

Micallef et al. ( 2020 ) highlight the role of the crowd in countering misinformation. They suspect that concerned citizens (i.e., the crowd), who use platforms where disinformation appears, can play a crucial role in spreading fact-checking information and in combating the spread of misinformation.

Recently Tchakounté et al. ( 2020 ) proposed a voting system as a new method of binary aggregation of opinions of the crowd and the knowledge of a third-party expert. The aggregator is based on majority voting on the crowd side and weighted averaging on the third-party site.

Similarly, Huffaker et al. ( 2020 ) propose a crowdsourced detection of emotionally manipulative language. They introduce an approach that transforms classification problems into a comparison task to mitigate conflation content by allowing the crowd to detect text that uses manipulative emotional language to sway users toward positions or actions. The proposed system leverages anchor comparison to distinguish between intrinsically emotional content and emotionally manipulative language.

La Barbera et al. ( 2020 ) try to understand how people perceive the truthfulness of information presented to them. They collect data from US-based crowd workers, build a dataset of crowdsourced truthfulness judgments for political statements, and compare it with expert annotation data generated by fact-checkers such as PolitiFact.

Coscia and Rossi ( 2020 ) introduce a crowdsourced flagging system that consists of online news flagging. The bipolar model of news flagging attempts to capture the main ingredients that they observe in empirical research on fake news and disinformation.

Unlike the previously mentioned researchers who focus on news content in their approaches, Pennycook and Rand ( 2019 ) focus on using crowdsourced judgments of the quality of news sources to combat social media disinformation.

Fact-Checking. The fact-checking task is commonly manually performed by journalists to verify the truthfulness of a given claim. Indeed, fact-checking features are being adopted by multiple online social network platforms. For instance, Facebook 34 started addressing false information through independent fact-checkers in 2017, followed by Google 35 the same year. Two years later, Instagram 36 followed suit. However, the usefulness of fact-checking initiatives is questioned by journalists 37 , as well as by researchers such as Andersen and Søe ( 2020 ). On the other hand, work is being conducted to boost the effectiveness of these initiatives to reduce misinformation (Chung and Kim 2021 ; Clayton et al. 2020 ; Nyhan et al. 2020 ).

Most researchers use fact-checking websites (e.g., politifact.com, 38 snopes.com, 39 Reuters, 40 , etc.) as data sources to build their datasets and train their models. Therefore, in the following, we specifically review examples of solutions that use fact-checking (Vlachos and Riedel 2014 ) to help build datasets that can be further used in the automatic detection of fake content.

Yang et al. ( 2019a ) use PolitiFact fact-checking website as a data source to train, tune, and evaluate their model named XFake, on political data. The XFake system is an explainable fake news detector that assists end users to identify news credibility. The fakeness of news items is detected and interpreted considering both content and contextual (e.g., statements) information (e.g., speaker).

Based on the idea that fact-checkers cannot clean all data, and it must be a selection of what “matters the most” to clean while checking a claim, Sintos et al. ( 2019 ) propose a solution to help fact-checkers combat problems related to data quality (where inaccurate data lead to incorrect conclusions) and data phishing. The proposed solution is a combination of data cleaning and perturbation analysis to avoid uncertainties and errors in data and the possibility that data can be phished.

Tchechmedjiev et al. ( 2019 ) propose a system named “ClaimsKG” as a knowledge graph of fact-checked claims aiming to facilitate structured queries about their truth values, authors, dates, journalistic reviews and other kinds of metadata. “ClaimsKG” designs the relationship between vocabularies. To gather vocabularies, a semi-automated pipeline periodically gathers data from popular fact-checking websites regularly.

AI-based Techniques

Previous work by Yaqub et al. ( 2020 ) has shown that people lack trust in automated solutions for fake news detection However, work is already being undertaken to increase this trust, for instance by von der Weth et al. ( 2020 ).

Most researchers consider fake news detection as a classification problem and use artificial intelligence techniques, as shown in Fig.  8 . The adopted AI techniques may include machine learning ML (e.g., Naïve Bayes, logistic regression, support vector machine SVM), deep learning DL (e.g., convolutional neural networks CNN, recurrent neural networks RNN, long short-term memory LSTM) and natural language processing NLP (e.g., Count vectorizer, TF-IDF Vectorizer). Most of them combine many AI techniques in their solutions rather than relying on one specific approach.

Examples of the most widely used AI techniques for fake news detection

Many researchers are developing machine learning models in their solutions for fake news detection. Recently, deep neural network techniques are also being employed as they are generating promising results (Islam et al. 2020 ). A neural network is a massively parallel distributed processor with simple units that can store important information and make it available for use (Hiriyannaiah et al. 2020 ). Moreover, it has been proven (Cardoso Durier da Silva et al. 2019 ) that the most widely used method for automatic detection of fake news is not simply a classical machine learning technique, but rather a fusion of classical techniques coordinated by a neural network.

Some researchers define purely machine learning models (Del Vicario et al. 2019 ; Elhadad et al. 2019 ; Aswani et al. 2017 ; Hakak et al. 2021 ; Singh et al. 2021 ) in their fake news detection approaches. The more commonly used machine learning algorithms (Abdullah-All-Tanvir et al. 2019 ) for classification problems are Naïve Bayes, logistic regression and SVM.

Other researchers (Wang et al. 2019c ; Wang 2017 ; Liu and Wu 2018 ; Mishra 2020 ; Qian et al. 2018 ; Zhang et al. 2020 ; Goldani et al. 2021 ) prefer to do a mixture of different deep learning models, without combining them with classical machine learning techniques. Some even prove that deep learning techniques outperform traditional machine learning techniques (Mishra et al. 2022 ). Deep learning is one of the most widely popular research topics in machine learning. Unlike traditional machine learning approaches, which are based on manually crafted features, deep learning approaches can learn hidden representations from simpler inputs both in context and content variations (Bondielli and Marcelloni 2019 ). Moreover, traditional machine learning algorithms almost always require structured data and are designed to “learn” to act by understanding labeled data and then use it to produce new results with more datasets, which requires human intervention to “teach them” when the result is incorrect (Parrish 2018 ), while deep learning networks rely on layers of artificial neural networks (ANN) and do not require human intervention, as multilevel layers in neural networks place data in a hierarchy of different concepts, which ultimately learn from their own mistakes (Parrish 2018 ). The two most widely implemented paradigms in deep neural networks are recurrent neural networks (RNN) and convolutional neural networks (CNN).

Still other researchers (Abdullah-All-Tanvir et al. 2019 ; Kaliyar et al. 2020 ; Zhang et al. 2019a ; Deepak and Chitturi 2020 ; Shu et al. 2018a ; Wang et al. 2019c ) prefer to combine traditional machine learning and deep learning classification, models. Others combine machine learning and natural language processing techniques. A few combine deep learning models with natural language processing (Vereshchaka et al. 2020 ). Some other researchers (Kapusta et al. 2019 ; Ozbay and Alatas 2020 ; Ahmed et al. 2020 ) combine natural language processing with machine learning models. Furthermore, others (Abdullah-All-Tanvir et al. 2019 ; Kaur et al. 2020 ; Kaliyar 2018 ; Abdullah-All-Tanvir et al. 2020 ; Bahad et al. 2019 ) prefer to combine all the previously mentioned techniques (i.e., ML, DL and NLP) in their approaches.

Table  11 , which is relegated to the Appendix (after the bibliography) because of its size, shows a comparison of the fake news detection solutions that we have reviewed based on their main approaches, the methodology that was used and the models.

Comparison of AI-based fake news detection techniques

Blockchain-based Techniques for Source Reliability and Traceability

Another research direction for detecting and mitigating fake news in social media focuses on using blockchain solutions. Blockchain technology is recently attracting researchers’ attention due to the interesting features it offers. Immutability, decentralization, tamperproof, consensus, record keeping and non-repudiation of transactions are some of the key features that make blockchain technology exploitable, not just for cryptocurrencies, but also to prove the authenticity and integrity of digital assets.

However, the proposed blockchain approaches are few in number and they are fundamental and theoretical approaches. Specifically, the solutions that are currently available are still in research, prototype, and beta testing stages (DiCicco and Agarwal 2020 ; Tchechmedjiev et al. 2019 ). Furthermore, most researchers (Ochoa et al. 2019 ; Song et al. 2019 ; Shang et al. 2018 ; Qayyum et al. 2019 ; Jing and Murugesan 2018 ; Buccafurri et al. 2017 ; Chen et al. 2018 ) do not specify which fake news type they are mitigating in their studies. They mention news content in general, which is not adequate for innovative solutions. For that, serious implementations should be provided to prove the usefulness and feasibility of this newly developing research vision.

Table  9 shows a classification of the reviewed blockchain-based approaches. In the classification, we listed the following:

• The type of fake news that authors are trying to mitigate, which can be multimedia-based or text-based fake news.
• The techniques used for fake news mitigation, which can be either blockchain only, or blockchain combined with other techniques such as AI, Data mining, Truth-discovery, Preservation metadata, Semantic similarity, Crowdsourcing, Graph theory and SIR model (Susceptible, Infected, Recovered).
• The feature that is offered as an advantage of the given solution (e.g., Reliability, Authenticity and Traceability). Reliability is the credibility and truthfulness of the news content, which consists of proving the trustworthiness of the content. Traceability aims to trace and archive the contents. Authenticity consists of checking whether the content is real and authentic.

A checkmark ( ✓ ) in Table  9 denotes that the mentioned criterion is explicitly mentioned in the proposed solution, while the empty dash (–) cell for fake news type denotes that it depends on the case: The criterion was either not explicitly mentioned (e.g., fake news type) in the work or the classification does not apply (e.g., techniques/other).

A classification of popular blockchain-based approaches for fake news detection in social media

After reviewing the most relevant state of the art for automatic fake news detection, we classify them as shown in Table  10 based on the detection aspects (i.e., content-based, contextual, or hybrid aspects) and the techniques used (i.e., AI, crowdsourcing, fact-checking, blockchain or hybrid techniques). Hybrid techniques refer to solutions that simultaneously combine different techniques from previously mentioned categories (i.e., inter-hybrid methods), as well as techniques within the same class of methods (i.e., intra-hybrid methods), in order to define innovative solutions for fake news detection. A hybrid method should bring the best of both worlds. Then, we provide a discussion based on different axes.

Fake news detection approaches classification

News content-based methods

Most of the news content-based approaches consider fake news detection as a classification problem and they use AI techniques such as classical machine learning (e.g., regression, Bayesian) as well as deep learning (i.e., neural methods such as CNN and RNN). More specifically, classification of social media content is a fundamental task for social media mining, so that most existing methods regard it as a text categorization problem and mainly focus on using content features, such as words and hashtags (Wu and Liu 2018 ). The main challenge facing these approaches is how to extract features in a way to reduce the data used to train their models and what features are the most suitable for accurate results.

Researchers using such approaches are motivated by the fact that the news content is the main entity in the deception process, and it is a straightforward factor to analyze and use while looking for predictive clues of deception. However, detecting fake news only from the content of the news is not enough because the news is created in a strategic intentional way to mimic the truth (i.e., the content can be intentionally manipulated by the spreader to make it look like real news). Therefore, it is considered to be challenging, if not impossible, to identify useful features (Wu and Liu 2018 ) and consequently tell the nature of such news solely from the content.

Moreover, works that utilize only the news content for fake news detection ignore the rich information and latent user intelligence (Qian et al. 2018 ) stored in user responses toward previously disseminated articles. Therefore, the auxiliary information is deemed crucial for an effective fake news detection approach.

Social context-based methods

The context-based approaches explore the surrounding data outside of the news content, which can be an effective direction and has some advantages in areas where the content approaches based on text classification can run into issues. However, most existing studies implementing contextual methods mainly focus on additional information coming from users and network diffusion patterns. Moreover, from a technical perspective, they are limited to the use of sophisticated machine learning techniques for feature extraction, and they ignore the usefulness of results coming from techniques such as web search and crowdsourcing which may save much time and help in the early detection and identification of fake content.

Hybrid approaches can simultaneously model different aspects of fake news such as the content-based aspects, as well as the contextual aspect based on both the OSN user and the OSN network patterns. However, these approaches are deemed more complex in terms of models (Bondielli and Marcelloni 2019 ), data availability, and the number of features. Furthermore, it remains difficult to decide which information among each category (i.e., content-based and context-based information) is most suitable and appropriate to be used to achieve accurate and precise results. Therefore, there are still very few studies belonging to this category of hybrid approaches.

Early detection

As fake news usually evolves and spreads very fast on social media, it is critical and urgent to consider early detection directions. Yet, this is a challenging task to do especially in highly dynamic platforms such as social networks. Both news content- and social context-based approaches suffer from this challenging early detection of fake news.

Although approaches that detect fake news based on content analysis face this issue less, they are still limited by the lack of information required for verification when the news is in its early stage of spread. However, approaches that detect fake news based on contextual analysis are most likely to suffer from the lack of early detection since most of them rely on information that is mostly available after the spread of fake content such as social engagement, user response, and propagation patterns. Therefore, it is crucial to consider both trusted human verification and historical data as an attempt to detect fake content during its early stage of propagation.

Conclusion and future directions

In this paper, we introduced the general context of the fake news problem as one of the major issues of the online deception problem in online social networks. Based on reviewing the most relevant state of the art, we summarized and classified existing definitions of fake news, as well as its related terms. We also listed various typologies and existing categorizations of fake news such as intent-based fake news including clickbait, hoax, rumor, satire, propaganda, conspiracy theories, framing as well as content-based fake news including text and multimedia-based fake news, and in the latter, we can tackle deepfake videos and GAN-generated fake images. We discussed the major challenges related to fake news detection and mitigation in social media including the deceptiveness nature of the fabricated content, the lack of human awareness in the field of fake news, the non-human spreaders issue (e.g., social bots), the dynamicity of such online platforms, which results in a fast propagation of fake content and the quality of existing datasets, which still limits the efficiency of the proposed solutions. We reviewed existing researchers’ visions regarding the automatic detection of fake news based on the adopted approaches (i.e., news content-based approaches, social context-based approaches, or hybrid approaches) and the techniques that are used (i.e., artificial intelligence-based methods; crowdsourcing, fact-checking, and blockchain-based methods; and hybrid methods), then we showed a comparative study between the reviewed works. We also provided a critical discussion of the reviewed approaches based on different axes such as the adopted aspect for fake news detection (i.e., content-based, contextual, and hybrid aspects) and the early detection perspective.

To conclude, we present the main issues for combating the fake news problem that needs to be further investigated while proposing new detection approaches. We believe that to define an efficient fake news detection approach, we need to consider the following:

• Our choice of sources of information and search criteria may have introduced biases in our research. If so, it would be desirable to identify those biases and mitigate them.
• News content is the fundamental source to find clues to distinguish fake from real content. However, contextual information derived from social media users and from the network can provide useful auxiliary information to increase detection accuracy. Specifically, capturing users’ characteristics and users’ behavior toward shared content can be a key task for fake news detection.
• Moreover, capturing users’ historical behavior, including their emotions and/or opinions toward news content, can help in the early detection and mitigation of fake news.
• Furthermore, adversarial learning techniques (e.g., GAN, SeqGAN) can be considered as a promising direction for mitigating the lack and scarcity of available datasets by providing machine-generated data that can be used to train and build robust systems to detect the fake examples from the real ones.
• Lastly, analyzing how sources and promoters of fake news operate over the web through multiple online platforms is crucial; Zannettou et al. ( 2019 ) discovered that false information is more likely to spread across platforms (18% appearing on multiple platforms) compared to valid information (11%).

Appendix: A Comparison of AI-based fake news detection techniques

This Appendix consists only in the rather long Table  11 . It shows a comparison of the fake news detection solutions based on artificial intelligence that we have reviewed according to their main approaches, the methodology that was used, and the models, as explained in Sect.  6.2.2 .

Author Contributions

The order of authors is alphabetic as is customary in the third author’s field. The lead author was Sabrine Amri, who collected and analyzed the data and wrote a first draft of the paper, all along under the supervision and tight guidance of Esma Aïmeur. Gilles Brassard reviewed, criticized and polished the work into its final form.

This work is supported in part by Canada’s Natural Sciences and Engineering Research Council.

Availability of data and material

Declarations.

On behalf of all authors, the corresponding author states that there is no conflict of interest.

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Contributor Information

Esma Aïmeur, Email: ac.laertnomu.ori@ruemia .

Sabrine Amri, Email: [email protected] .

Gilles Brassard, Email: ac.laertnomu.ori@drassarb .

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Q&A: How – and why – we’re changing the way we study tech adoption

What share of U.S. adults have high-speed internet at home ? Own a smartphone? Use social media ?

Pew Research Center has long studied tech adoption by interviewing Americans over the phone. But starting with the publications released today, we’ll be reporting on these topics using our National Public Opinion Reference Survey (NPORS) instead. The biggest difference: NPORS participants are invited by postal mail and can respond to the survey via a paper questionnaire or online, rather than by phone.

To explain the thinking behind this change and its implications for our future work, here’s a conversation with Managing Director of Internet and Technology Research Monica Anderson and Research Associate Colleen McClain. This interview has been condensed and edited for clarity.

Pew Research Center has been tracking tech adoption in the United States for decades. Why is this area of study so important?

Anderson: We see this research as foundational to understanding the broader impact that the internet, mobile technology and social media have on our society.

Americans have an array of digital tools that help them with everything from getting news to shopping to finding jobs. Studying how people are going online, which devices they own and which social media sites they use is crucial for understanding how they experience the world around them.

This research also anchors our ongoing work on the digital divide : the gap between those who have access to certain technologies and those who don’t. It shows us where demographic differences exist, if they’ve changed over time, and how factors like age, race and income may contribute.

Our surveys are an important reminder that some technologies, like high-speed internet, remain out of reach for some Americans, particularly those who are less affluent. In fact, our latest survey shows that about four-in-ten Americans living in lower-income households do not subscribe to home broadband.

Why is your team making the switch from phone surveys to the National Public Opinion Reference Survey (NPORS)?

McClain: The internet hasn’t just transformed Americans’ everyday lives – it’s also transformed the way researchers study its impact. The changes we’ve made this year set us up to continue studying tech adoption long into the future.

We began tracking Americans’ tech use back in 2000. At that point, about half of Americans were online, and just 1% had broadband at home. Like much of the survey research world, we relied on telephone polling for these studies, and this approach served us well for decades.

But in more recent years, the share of people who respond to phone polls has plummeted , and these types of polls have become more costly. At the same time, online surveys have become more popular and pollsters’ methods have become more diverse . This transformation in polling is reflected in our online American Trends Panel , which works well for the vast majority of the Center’s U.S. survey work.

But there’s a caveat: Online-only surveys aren’t always the best approach when it comes to measuring certain types of data points. That includes measuring how many people don’t use technology in the first place.

Enter the National Public Opinion Reference Survey, which the Center launched in 2020 to meet these kinds of challenges. By giving people the choice to take our survey on paper or online, it is especially well-suited for hearing from Americans who don’t use the internet, aren’t comfortable with technology or just don’t want to respond online. That makes it a good fit for studying the digital divide. And NPORS achieves a higher response rate than phone polls .  

Shifting our tech adoption studies to NPORS ensures we’re keeping up with the latest advances in the Center’s methods toolkit, with quality at the forefront of this important work.

The internet hasn’t just transformed Americans’ everyday lives – it’s also transformed the way researchers study its impact. The changes we’ve made this year set us up to continue studying tech adoption long into the future. Colleen McClain

Are the old and new approaches comparable?

McClain: We took several steps to make our NPORS findings as comparable as possible with our earlier phone surveys. We knew that it can be tricky, and sometimes impossible, to directly compare the results of surveys that use different modes – that is, methods of interviewing. How a survey is conducted can affect how people answer questions and who responds in the first place. These are known as “mode effects.”

To try to minimize the impact of this change, we started by doing what we do best: gathering data.

Around the same time that we fielded our phone polls about tech adoption in 2019 and 2021, we also fielded some surveys using alternate approaches. We didn’t want to change the mode right away, but rather understand how any changes in our approach might affect the data we were collecting about how Americans use technology.

These test runs helped narrow our options and tweak the NPORS design. Using the 2019 and 2021 phone data we collected as a comparison point, we worked over the next few years to make the respondent experience as similar as possible across modes.

What does your new approach mean for your ability to talk about changes over time?

McClain: We carefully considered the potential for mode effects as we decided how to talk about the changes we saw in our findings this year. Even with all the work we did to make the approaches as comparable as possible, we wanted to be cautious.

For instance, we paid close attention to the size of any changes we observed. In some cases, the figures were fairly similar between 2021 and 2023, and even without the mode shift, we wouldn’t make too much of them.

We gave a thorough look at more striking differences. For example, 21% of Americans said they used TikTok in our 2021 phone survey, and that’s risen to 33% now in our paper/online survey. Going back to our test runs from earlier years helped us conclude it’s unlikely this change was all due to mode. We believe it also reflects real change over time.

While the mode shift makes it trickier than usual to talk about trends, we believe the change in approach is a net positive for the quality of our work. NPORS sets us up well for the future.

How are you communicating this mode shift in your published work?

McClain: It’s important to us that readers can quickly and easily understand the shift and when it took place.

In some cases, we’ll be displaying the findings from our paper/online survey side by side with the data points from prior phone surveys. Trend charts in our reports signal the mode shift with a dotted line to draw attention to the change in approach. In our fact sheets , a vertical line conveys the same thing. In both cases, we also provide information in the footnotes below the chart itself.

In other places in our publications, we’re taking an even more cautious approach and focusing on the new data rather than on trends.

Did you have to change the way you asked survey questions?

McClain: Writing questions that keep up with the ever-changing nature of technology is always a challenge, and the mode shift complicated this further. For example, our previous phone surveys were conducted by interviewers, but taking surveys online or on paper doesn’t involve talking to someone. We needed to adapt our questions to keep the experience as consistent as possible on the new paper and online surveys.

Take who subscribes to home broadband, for example. Knowing we wouldn’t have an interviewer to probe and confirm someone’s response in the new modes, we tested out different options in advance to help us ensure we were collecting quality data.

In this case, we gave people a chance to say they were “not sure” or to write in a different type of internet connection, if the ones we offered didn’t quite fit their situation. We also updated the examples of internet connections in the question to be consistent with evolving technology.

Which findings from your latest survey stand out to you?

Anderson: There are several exciting things in our latest work, but two findings related to social media really stand out.

The first is the rise of TikTok. A third of U.S. adults – including about six-in-ten adults under 30 – use this video-based platform. These figures have significantly jumped since we last asked these questions in 2021. And separate surveys from the Center have found that TikTok is increasingly becoming a news source for Americans , especially young adults.

The second is how dominant Facebook remains. While its use has sharply declined among teens in the U.S. , most adults – about two-thirds – say they use the site. And this share has remained relatively stable over the past decade or so. YouTube is the only platform we asked about in our current survey that is more widely used than Facebook.

These findings reinforce why consistently tracking the use of technology, especially specific sites and apps, is so important. The online landscape can evolve quickly. As researchers who study these platforms, a forward-looking mindset is key. We’ll continue looking for new and emerging platforms while tracking longer-standing sites to see how use changes – or doesn’t – over time.

To learn more about the National Public Opinion Reference Survey, read our NPORS fact sheet . For more on Americans’ use of technology, read our new reports:

• Americans’ Use of Mobile Technology and Home Broadband
• Americans’ Social Media Use
• Internet & Technology
• Research Explainers
• Survey Methods
• Technology Adoption

6 facts about Americans and TikTok

Many americans think generative ai programs should credit the sources they rely on, americans’ use of chatgpt is ticking up, but few trust its election information, whatsapp and facebook dominate the social media landscape in middle-income nations, 5 facts about americans and sports, most popular.

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New study finds that Black and Latinx youth online engagement can foster a positive sense of self

Building on data from a project led by USC Rossier professor Brendesha Tynes, Naila Smith is the lead author of a new research paper that examines how online spaces created by Black and Latinx youth benefit their ethnic-racial identity development.

With social media use among many youth nearly constant, we often read reports of the adversities that young people encounter online, from impacts on their mental health to the dangers that meeting malicious strangers in real life can pose. While the negative effects of social media usage for teens should not be discounted, there are some benefits for Black and Latinx youth in particular as they navigate their ethnic-racial identity (ERI) online. Brendesha M. Tynes, Dean's Professor of Educational Equity at the USC Rossier School of Education, is a co-author of a newly published article led by Naila A. Smith, assistant professor of education at the University of Virginia, and supported by Daisy E. Camacho-Thompson, that shows how the race/ethnic- and civic-related online experiences of Black and Latinx adolescents are connected to their ERI development.

Black and Latinx youth tend to spend more time engaged online than their White peers, and they also spend more time than youth of other ethnic-racial groups making sense of what their ERI means to them and engaging civically and politically online. Exploring and seeking out information about one’s race/ethnicity, refining how one understands one’s race/ethnicity, and feeling positively or negatively about one’s race/ethnicity are factors in ERI development. 

Research on Black and Latinx adolescents’ online experiences has shown that they do face additional risks such as online racial discrimination and hate and viewing traumatic racial content. Online social connection can help Black and Latinx youth cultivate a sense of belonging to a social group that may help them make meaning of their identity as they encounter some of the harms in online spaces. This newly published article builds on some of Tynes’ previous work about how content youth are exposed to online impacts their ERI exploration.

Using data from the Teen Life Online and in Schools Project (TLOS), which Tynes directs, Smith and her team found that youth who can establish relationships online with friends of the same race/ethnicity experience more adaptive outcomes over time, meaning that in fostering online relationships with same-age, same ethnic/racial group peers these young people can better cope with some of the harmful information that they encounter online. 

Tynes’s TLOS data was one of the first datasets to investigate teens' online experiences across multiple years (three years) using both survey and interview data and a multi-racial sample of adolescents. While Tynes created the dataset, her collaborators and mentees led studies that were not previously included in the original proposal. 

Smith’s research interests include the development of racial and ethnically minoritized youth over time. She was curious about youths’ experiences in the online space over time and sought to examine the role of socio-cultural resources and assets in the experiences of Black and Latinx youth.  

“I was really interested in examining what factors contributed to ethnic-racial identity development, which is a sociocultural asset for Black and Latinx youth. In examining aspects of youths’ race- and civic-related online experiences and looking at how young people curate or create online spaces that meet their needs, we were able to show that there are these different ways that Black and Latinx adolescents are engaging online in their exploration of their world and their ethnic-racial background,” said Smith.

The study also found that earlier online activity is connected with ERI development one year later, meaning that race and civic-related online activities are important for young people’s feelings and behaviors in terms of their future ERI. Having a clearer idea of what their ERI means to Black and Latinx youth is associated with having better outcomes academically and mental health benefits.

“A major goal of the TLOS Project was to see what cultural resources youth bring to online spaces that might buffer them from some of the negative outcomes we might see that have been published in journals and in news articles. I wanted to paint a more holistic view of young people’s experiences online,” said Tynes. “Most of what we have published has been on the negative side, but I’m excited to have this manuscript focus on the positive experiences that young people are having online.” 

Smith started her collaboration with Tynes because of her advocacy for students and her deep knowledge of youth development. The two met at an academic conference when Smith was a graduate student.

“I wanted to meet Brendesha specifically because she was the foremost scholar on the online experiences of Black and Latinx youth, and I wanted to develop my expertise in the role of the internet in youth development. Her tremendous productivity and creativity are built on deep knowledge of a wide range of fields that inform her innovation in thinking about how we can support Black and Brown youth in their development specifically in online spaces,” concluded Smith. 

Smith and her co-authors want teachers and parents to know that Black and Latinx youth can benefit positively from the time they spend online, and that access to online information and experiences can support meaningful identity exploration. According to Smith, parents and teachers can help guide youth in their online ERI exploration and engage them in conversations about what they are learning to help them process the information that they are consuming. In schools where Black and Latinx youth may be in the minority or where they may not have access to materials in the curriculum that allow them to learn about their cultures and identities, supporting online activities around ERI is even more critical.

“With new laws banning certain books or talking about Black people’s history in the classroom in some states, people have to make an effort to make sure that kids are getting what they need to explore who they are,” said Tynes. “The digital literacy that young people need to sift through stereotypes and misinformation about their racial-ethnic group becomes more important in these places where the students cannot rely on their teachers to provide accurate information.”

The team’s findings may be used to support the creation of interventions to help adolescents create and curate online spaces where they can meet peers in their age range and ethnic/racial groups to help them with their relationship-building skills.

“Instead of the internet being a place where people are in constant danger, youth can craft spaces where their experiences are actually beneficial for their development, and these young people are doing that on their own,” said Tynes . “Parents and educators can support that exploration and provide guidance.”

Tynes is supporting that guidance by building a digital literacy and mental health intervention with a $4.6 million Transformational Research Award NIH grant. With the funds, Tynes seeks to research and provide adolescents with tools to cope with the negative messages they receive online and the skills to use digital media as a tool to excel in school. Tynes’s goal is to help youth thrive in their everyday lives as they navigate digital spaces. The newly designed, first-of-its-kind platform will have several modules and virtual reality experiences to help adolescents practice how to respond to some of their experiences online. The alpha version of this intervention is scheduled to launch in September 2024.

Brendesha Tynes

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April 4, 2024

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5 tips to enhance your research paper’s visibility and altmetric score.

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I previously wrote about the importance of attracting public attention to scientific research . In today’s world, where billions of people are attached to their digital devices watching the very addictive but often useless TikTok content or receiving instant gratification by engaging in meaningless debates about celebrities, scientists need to find creative ways to have their research noticed. Popularizing scientific research helps inspire the younger generations to go into science and provide the general public with a sense of optimism enabling the government to channel more resources into science. People do need inspiration. But very often, even very important scientific breakthroughs requiring many years, hard work, skill, funding, and genuine serendipity go largely unnoticed by the general public.

One of the best ways to measure expert and public attention is the cumulative Altmetric Attention Score , originally developed by Digital Science and adopted by many prestigious publishers, including Nature Publishing Group. Every Nature paper and the papers published by pretty much every credible publisher are tracked by Digital Science by the Document Object Identification (DOI) or the Unique Resource Locator (URL) . While Altmetric has many limitations, for example, it does not track LinkedIn posts and may not adequately cover the impact of top-tier media coverage, at the moment it is the blueprint for tracking attention.

Altmetric Score in The Age of Generative AI

Media attention is likely to be very important in the age of generative AI. Many modern generative systems, such as ChatGPT, Claude, Mistral, and Gemini, as well as hundreds of Large Language Models (LLMs) in China, use the data from the same sources referenced in Altmetric to learn. The more times generative systems see the same concept presented in different contexts, the better they learn. So if you want to contribute to the training of AI systems that may thank you for it in the future - Altmetric is the way to go.

So what can a research group do to ensure they are communicating their findings effectively and increasing the visibility of their research to ensure it gets reflected in the Altmetric Attention Score?

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Altmetric openly discloses the weights of the various sources and the scoring algorithm is relatively straightforward. It is easy to learn, and there are multiple online resources providing advice on how to share your research in ways that will be captured by Altmetric. Cambridge University Press published a guideline to Altmetric for the authors on how to popularize their research with Altmetric in mind. Wolters Kluwer put out a guide and the editor of Toxicology and Pathology wrote a comprehensive overview of Altmetric and how to use it. Surprisingly, this overview got an Altmetric Attention Score of only 4 at the time of the writing, but was cited 137 times according to Google Scholar .

Altmetric monitors social networks, including X (formerly Twitter), Facebook, and Reddit; all major top-tier mainstream media, mainstream science blogs, policy documents, patents, Wikipedia articles, peer review websites, F1000, Syllabi, X (formerly Twitter), tracked Facebook pages, Reddit, one of the Stack Exchange sites, and Youtube. Unfortunately, several powerful platforms, including LinkedIn, are not currently tracked.

The popularity of the paper depends on many factors. Firstly, it has to be novel, trendy, and newsworthy. You are unlikely to get high Altmetric Score with a boring topic. Secondly, papers coming out of popular labs in top-tier academic institutions and in top journals are likely to attract more attention. Often, the communications officers in these academic institutions work closely with the media to amplify notable research. Celebrity companies, for example, Google DeepMind, consistently get higher coverage.

Screenshot of the Altmetric Attention Score "Flower" showing several tracked sources

Here are the five tips for increasing the visibility of your work and ensuring that reach is tracked and reflected by Altmetric:

1. Understand How Altmetric System Works

Congratulations, if you read this article and looked at what sources are tracked by Altmetric. Most likely, you got the basics and will be able to get a “balanced flower” by making a press release, tweeting the DOI of the paper on X, posting a video overview of your paper on Youtube, announcing on Reddit (I still need to learn how to do this).

To understand how Altmetric works, I emailed a few questions to Miguel Garcia, Director of Product and Data Analytics Hub at Digital Science and my first question was wether the Altmetric algorithm is open source. “The Altmetric Attention Score's calculation is not open source but we try to provide as much information as possible around how we calculate it here, and are currently considering what steps we might take to make our algorithms more transparent.” He also provided a link to how the Altmetric Attention Score is calculated.

Many professionals use LinkedIn as the primary social media resource and I was wondering why Altmetric stopped tracking it. Bad news - technical reasons prevent tracking DOIs on LinkedIn. Good news - they are actively seeking ways to appropriately track mentions on LinkedIn and we may see some news toward the end of the year.

My other big question was how does Altmetric count tweets and retweets on X. What if there are many posts from the same account? Miguel’s response was: “Re-tweets count less than original tweets. In addition to that, modifiers are applied to the type of account that is tweeting in order to reduce the weight of the tweet in situations where we find signals of bias or promiscuity (for example a journal publisher only tweeting their own articles). Besides that, we have conditions around the maximum number of retweets in order to limit the maximum impact they would have.”

So tweeting the article many times will not help you. But if other scientists tweet you paper with a DOI - these tweets will get counted. So tweet others as you would like to be tweeted.

2. Make a Press Release and Distribute to Science-focused Media

If your paper is significant, for example, you elucidated novel disease biology, discovered a new drug, developed a new fancy algorithm, designed a new material, or developed a new application for a quantum computer, it is worthwhile investing some time and resources in writing a press release. If you are working for an academic institution, most likely they have a communications office that will help you. If you do not have this luxury, you will need to learn how to write a press release. Plenty of free online guides cover the basics of press release writing. And press releases are one area where ChatGPT and other generative tools do surprisingly well. Upload your paper and ask it to write a press release, check for errors or exaggerations, edit, and you are ready to go. Just make sure to include the DOI and the URL of your paper. A proper business press release on BusinessWire or PRNewswire may cost several thousand dollars. In my opinion, these resources are dramatically overcharging while providing little service. I don't remember a case where a journalist picked up our news based on a commercial press release. But these releases are often reposted by other online press release distributors and the boost to Altmetric may be considerable. The default news release distribution service for research news is EurekAlert. This resource may sometimes result in journalistic coverage as many reporters are using it for science news. There are many free resources you can use if you do not have any budget.

Once the press release is issued, share it with the media. Share the resulting news coverage via your social networks and contacts. Many journalists track the popularity of their news articles and giving them several thousand extra views from professional audience and increasing their social following increases the chances that they will cover the next important research paper.

3. Make a Blog Post

Writing a blog post can be longer and more comprehensive than the press release. Make sure to add fancy diagrams and graphical explainers. You can share the blog post with the journalists at the same time as the press release. Your blog may serve as a source of inspiration for third party news coverage. Make sure to reference the DOI and URL of your paper.

If your paper is in one of the Nature journals, consider writing a “Behind the Paper" blog post on Nature Bioengineering Community. Surprisingly, these blogs are rarely picked up by Altmetric but may serve as a source of inspiration for the journalists and social media influencers. Plus, it is a resource by the Nature Publishing Group.

4. Tweet and Ask Your Team Members to Tweet

Each post on X gives you a quarter of an Altmetric point. If your paper goes viral on X, your Altmetric score can be considerable. Plus, once journalists notice that it went viral, they will be more likely to cover the story, further increasing the score.

Try to choose the time of the post, the hashtags, and the images wisely. Since Elon Musk took over X and opened the algorithm it became very transparent and easy to optimize for. Here are the top 10 tips for boosting attention for a post on X. Make sure to include the DOI or the URL of the paper for Altmetric to find the post.

5. Experiment, Learn, Repeat

My highest Altmetric Attention Score core to date was around 1,500 for a paper in Nature Biotechnology published in 2019, where we used a novel method for designing small molecules called Generative Tensorial Reinforcement Learning (GENTRL) to generate new molecules with druglike properties that got synthesized and tested all the way into mice. In 2024, we went further and showed that an AI-generated molecule for an AI-discovered target was tested all the way up to Phase II human trials, but the paper published in Nature Biotechnology, let’s call it the TNIK paper , has achieved a score ofjust over 600 to date. So what has changed and what can we learn from these two papers?

The popularity of the paper depends on many factors. Ones which capture the public imagination or have widespread appeal are of course, much more likely to gain traction online. When we published the GENTRL paper in 2019, Generative AI was in its infancy, and there are pretty much no other companies that I heard of at the intersection of generative AI and drug discovery. We also published multiple articles in this field in the years leading to that paper and many key opinion leaders (KOLs) followed us. That following included a small army of generative AI skeptics who not only contributed to multiple rejections in peer-reviewed journals but also openly criticized this approach in social networks. This criticism also helped boost the Altmetric Score and bring more attention to the study. So first learning from this exercise - negative publicity helps overall publicity. As long as you are certain that your research results are honest - leave room for criticism and even help expose your paper’s weaknesses. Critics are your greatest Altmetric boosters. Since readers and, by extension journalists, react to negative news and drama stronger than to positive news, critical reviews will boost your Altmetric as long as the DOI or URL of the paper is properly referenced.

Secondly, papers coming out of popular labs in top-tier academic institutions and in top journals are likely to attract more attention. Often, the communications officers in these academic institutions work closely with the media to amplify notable research. Celebrity companies, for example, Google DeepMind, always get a higher level of coverage. For example, the AlphaFold paper published in July 2021 in Nature got an Altmetric Attention Score of over 3,500 . Even though I have not seen any drugs out of AlphaFold reaching preclinical candidate status, I predict the popularity of this tool will result in the first Nobel Prize in this area. Therefore, in order to become famous and popularize your research more effectively, it is a good idea to build up the public profile of yourself and your work. For example, Kardashians are famous for being famous .

Be careful with Wikipedia. I made a mistake explaining the importance of Wikipedia to students when lecturing on the future of generative AI, and one or two of them got banned for expanding the articles with paper references. Wikipedia requires that these are added by independent editors rather than the authors of papers themselves, but if some editors do not like it, they will not go deep or investigate - they will assume wrongdoing. So it is better to avoid even talking about Wikipedia. References there should happen naturally and often some of the more popular papers get picked up and referenced by veteran editors.

Experimenting with Altmetric will also help you explore new strategies for popularizing scientific research and develop new strategies for inspiring people to learn or even get into the new exciting field. UNESCO estimates that there was just over 8 million full-time equivalent (FTE) researchers in 2018 globally. Only a fraction of these are in biotechnology - less than 0.01% of the global population. If you motivate a million students to go into biotechnology by popularizing your research, you double this number.

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Paper: To understand cognition—and its dysfunction—neuroscientists must learn its rhythms

Thought emerges and is controlled in the brain via the rhythmically and spatially coordinated activity of millions of neurons, scientists argue in a new article. Understanding cognition and its disorders requires studying it at that level.

It could be very informative to observe the pixels on your phone under a microscope, but not if your goal is to understand what a whole video on the screen shows. Cognition is much the same kind of emergent property in the brain . It can only be understood by observing how millions of cells act in coordination, argues a trio of MIT neuroscientists. In a new article , they lay out a framework for understanding how thought arises from the coordination of neural activity driven by oscillating electric fields—also known as brain “waves” or “rhythms.”

Historically dismissed solely as byproducts of neural activity, brain rhythms are actually critical for organizing it, write Picower Professor Earl Miller and research scientists Scott Brincat and Jefferson Roy in Current Opinion in Behavioral Science . And while neuroscientists have gained tremendous knowledge from studying how individual brain cells connect and how and when they emit “spikes” to send impulses through specific circuits, there is also a need to appreciate and apply new concepts at the brain rhythm scale, which can span individual, or even multiple, brain regions.

“Spiking and anatomy are important but there is more going on in the brain above and beyond that,” said senior author Miller, a faculty member in The Picower Institute for Learning and Memory and the Department of Brain and Cognitive Sciences at MIT. “There’s a whole lot of functionality taking place at a higher level, especially cognition.”

The stakes of studying the brain at that scale, the authors write, might not only include understanding healthy higher-level function but also how those functions become disrupted in disease.

“Many neurological and psychiatric disorders, such as schizophrenia, epilepsy and Parkinson’s involve disruption of emergent properties like neural synchrony,” they write. “We anticipate that understanding how to interpret and interface with these emergent properties will be critical for developing effective treatments as well as understanding cognition.”

The emergence of thoughts

The bridge between the scale of individual neurons and the broader-scale coordination of many cells is founded on electric fields, the researchers write. Via a phenomenon called “ephaptic coupling,” the electrical field generated by the activity of a neuron can influence the voltage of neighboring neurons, creating an alignment among them. In this way, electric fields both reflect neural activity but also influence it. In a paper in 2022 , Miller and colleagues showed via experiments and computational modeling that the information encoded in the electric fields generated by ensembles of neurons can be read out more reliably than the information encoded by the spikes of individual cells. In 2023 Miller’s lab provided evidence that rhythmic electrical fields may coordinate memories between regions.

At this larger scale, in which rhythmic electric fields carry information between brain regions, Miller’s lab has published numerous studies showing that lower-frequency rhythms in the so-called “beta” band originate in deeper layers of the brain’s cortex and appear to regulate the power of faster-frequency “gamma” rhythms in more superficial layers. By recording neural activity in the brains of animals engaged in working memory games the lab has shown that beta rhythms carry “top down” signals to control when and where gamma rhythms can encode sensory information, such as the images that the animals need to remember in the game.

Some of the lab’s latest evidence suggests that beta rhythms apply this control of cognitive processes to physical patches of the cortex, essentially acting like stencils that pattern where and when gamma can encode sensory information into memory, or retrieve it. According to this theory, which Miller calls “ Spatial Computing ,” beta can thereby establish the general rules of a task (for instance, the back and forth turns required to open a combination lock), even as the specific information content may change (for instance, new numbers when the combination changes). More generally, this structure also enables neurons to flexibly encode more than one kind of information at a time, the authors write, a widely observed neural property called “mixed selectivity.” For instance, a neuron encoding a number of the lock combination can also be assigned, based on which beta-stenciled patch it is in, the particular step of the unlocking process that the number matters for.

In the new study Miller, Brincat and Roy suggest another advantage consistent with cognitive control being based on an interplay of large-scale coordinated rhythmic activity: “Subspace coding.” This idea postulates that brain rhythms organize the otherwise massive number of possible outcomes that could result from, say, 1,000 neurons engaging in independent spiking activity. Instead of all the many combinatorial possibilities, many fewer “subspaces” of activity actually arise, because neurons are coordinated, rather than independent. It is as if the spiking of neurons is like a flock of birds coordinating their movements.  Different phases and frequencies of brain rhythms provide this coordination, aligned to amplify each other, or offset to prevent interference. For instance, if a piece of sensory information needs to be remembered, neural activity representing it can be protected from interference when new sensory information is perceived.

“Thus the organization of neural responses into subspaces can both segregate and integrate information,” the authors write.

The power of brain rhythms to coordinate and organize information processing in the brain is what enables functional cognition to emerge at that scale, the authors write. Understanding cognition in the brain, therefore, requires studying rhythms.

“Studying individual neural components in isolation—individual neurons and synapses—has made enormous contributions to our understanding of the brain and remains important,” the authors conclude. “However, it’s becoming increasingly clear that, to fully capture the brain’s complexity, those components must be analyzed in concert to identify, study, and relate their emergent properties.”

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Title: a simple strategy for body estimation from partial-view images.

Abstract: Virtual try-on and product personalization have become increasingly important in modern online shopping, highlighting the need for accurate body measurement estimation. Although previous research has advanced in estimating 3D body shapes from RGB images, the task is inherently ambiguous as the observed scale of human subjects in the images depends on two unknown factors: capture distance and body dimensions. This ambiguity is particularly pronounced in partial-view scenarios. To address this challenge, we propose a modular and simple height normalization solution. This solution relocates the subject skeleton to the desired position, thereby normalizing the scale and disentangling the relationship between the two variables. Our experimental results demonstrate that integrating this technique into state-of-the-art human mesh reconstruction models significantly enhances partial body measurement estimation. Additionally, we illustrate the applicability of this approach to multi-view settings, showcasing its versatility.

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