hypothesis on mass media

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2.2 Media Effects Theories

Learning objectives.

• Identify the basic theories of media effects.
• Explain the uses of various media effects theories.

Early media studies focused on the use of mass media in propaganda and persuasion. However, journalists and researchers soon looked to behavioral sciences to help figure out the effect of mass media and communications on society. Scholars have developed many different approaches and theories to figure this out. You can refer to these theories as you research and consider the media’s effect on culture.

Widespread fear that mass-media messages could outweigh other stabilizing cultural influences, such as family and community, led to what is known as the direct effects model of media studies. This model assumed that audiences passively accepted media messages and would exhibit predictable reactions in response to those messages. For example, following the radio broadcast of War of the Worlds in 1938 (which was a fictional news report of an alien invasion), some people panicked and believed the story to be true.

Challenges to the Direct Effects Theory

The results of the People’s Choice Study challenged this model. Conducted in 1940, the study attempted to gauge the effects of political campaigns on voter choice. Researchers found that voters who consumed the most media had generally already decided for which candidate to vote, while undecided voters generally turned to family and community members to help them decide. The study thus discredited the direct effects model and influenced a host of other media theories (Hanson, 2009). These theories do not necessarily give an all-encompassing picture of media effects but rather work to illuminate a particular aspect of media influence.

Marshall McLuhan’s Influence on Media Studies

During the early 1960s, English professor Marshall McLuhan wrote two books that had an enormous effect on the history of media studies. Published in 1962 and 1964, respectively, the Gutenberg Galaxy and Understanding Media both traced the history of media technology and illustrated the ways these innovations had changed both individual behavior and the wider culture. Understanding Media introduced a phrase that McLuhan has become known for: “The medium is the message.” This notion represented a novel take on attitudes toward media—that the media themselves are instrumental in shaping human and cultural experience.

His bold statements about media gained McLuhan a great deal of attention as both his supporters and critics responded to his utopian views about the ways media could transform 20th-century life. McLuhan spoke of a media-inspired “global village” at a time when Cold War paranoia was at its peak and the Vietnam War was a hotly debated subject. Although 1960s-era utopians received these statements positively, social realists found them cause for scorn. Despite—or perhaps because of—these controversies, McLuhan became a pop culture icon, mentioned frequently in the television sketch-comedy program Laugh-In and appearing as himself in Woody Allen’s film Annie Hall .

The Internet and its accompanying cultural revolution have made McLuhan’s bold utopian visions seem like prophecies. Indeed, his work has received a great deal of attention in recent years. Analysis of McLuhan’s work has, interestingly, not changed very much since his works were published. His supporters point to the hopes and achievements of digital technology and the utopian state that such innovations promise. The current critique of McLuhan, however, is a bit more revealing of the state of modern media studies. Media scholars are much more numerous now than they were during the 1960s, and many of these scholars criticize McLuhan’s lack of methodology and theoretical framework.

Despite his lack of scholarly diligence, McLuhan had a great deal of influence on media studies. Professors at Fordham University have formed an association of McLuhan-influenced scholars. McLuhan’s other great achievement is the popularization of the concept of media studies. His work brought the idea of media effects into the public arena and created a new way for the public to consider the influence of media on culture (Stille, 2000).

Agenda-Setting Theory

In contrast to the extreme views of the direct effects model, the agenda-setting theory of media stated that mass media determine the issues that concern the public rather than the public’s views. Under this theory, the issues that receive the most attention from media become the issues that the public discusses, debates, and demands action on. This means that the media is determining what issues and stories the public thinks about. Therefore, when the media fails to address a particular issue, it becomes marginalized in the minds of the public (Hanson).

When critics claim that a particular media outlet has an agenda, they are drawing on this theory. Agendas can range from a perceived liberal bias in the news media to the propagation of cutthroat capitalist ethics in films. For example, the agenda-setting theory explains such phenomena as the rise of public opinion against smoking. Before the mass media began taking an antismoking stance, smoking was considered a personal health issue. By promoting antismoking sentiments through advertisements, public relations campaigns, and a variety of media outlets, the mass media moved smoking into the public arena, making it a public health issue rather than a personal health issue (Dearing & Rogers, 1996). More recently, coverage of natural disasters has been prominent in the news. However, as news coverage wanes, so does the general public’s interest.

Through a variety of antismoking campaigns, the health risks of smoking became a public agenda.

Quinn Dombrowski – Weapons of mass destruction – CC BY-SA 2.0.

Media scholars who specialize in agenda-setting research study the salience, or relative importance, of an issue and then attempt to understand what causes it to be important. The relative salience of an issue determines its place within the public agenda, which in turn influences public policy creation. Agenda-setting research traces public policy from its roots as an agenda through its promotion in the mass media and finally to its final form as a law or policy (Dearing & Rogers, 1996).

Uses and Gratifications Theory

Practitioners of the uses and gratifications theory study the ways the public consumes media. This theory states that consumers use the media to satisfy specific needs or desires. For example, you may enjoy watching a show like Dancing With the Stars while simultaneously tweeting about it on Twitter with your friends. Many people use the Internet to seek out entertainment, to find information, to communicate with like-minded individuals, or to pursue self-expression. Each of these uses gratifies a particular need, and the needs determine the way in which media is used. By examining factors of different groups’ media choices, researchers can determine the motivations behind media use (Papacharissi, 2009).

A typical uses and gratifications study explores the motives for media consumption and the consequences associated with use of that media. In the case of Dancing With the Stars and Twitter, you are using the Internet as a way to be entertained and to connect with your friends. Researchers have identified a number of common motives for media consumption. These include relaxation, social interaction, entertainment, arousal, escape, and a host of interpersonal and social needs. By examining the motives behind the consumption of a particular form of media, researchers can better understand both the reasons for that medium’s popularity and the roles that the medium fills in society. A study of the motives behind a given user’s interaction with Facebook, for example, could explain the role Facebook takes in society and the reasons for its appeal.

Uses and gratifications theories of media are often applied to contemporary media issues. The analysis of the relationship between media and violence that you read about in preceding sections exemplifies this. Researchers employed the uses and gratifications theory in this case to reveal a nuanced set of circumstances surrounding violent media consumption, as individuals with aggressive tendencies were drawn to violent media (Papacharissi, 2009).

Symbolic Interactionism

Another commonly used media theory, symbolic interactionism , states that the self is derived from and develops through human interaction. This means the way you act toward someone or something is based on the meaning you have for a person or thing. To effectively communicate, people use symbols with shared cultural meanings. Symbols can be constructed from just about anything, including material goods, education, or even the way people talk. Consequentially, these symbols are instrumental in the development of the self.

This theory helps media researchers better understand the field because of the important role the media plays in creating and propagating shared symbols. Because of the media’s power, it can construct symbols on its own. By using symbolic interactionist theory, researchers can look at the ways media affects a society’s shared symbols and, in turn, the influence of those symbols on the individual (Jansson-Boyd, 2010).

One of the ways the media creates and uses cultural symbols to affect an individual’s sense of self is advertising. Advertisers work to give certain products a shared cultural meaning to make them desirable. For example, when you see someone driving a BMW, what do you think about that person? You may assume the person is successful or powerful because of the car he or she is driving. Ownership of luxury automobiles signifies membership in a certain socioeconomic class. Equally, technology company Apple has used advertising and public relations to attempt to become a symbol of innovation and nonconformity. Use of an Apple product, therefore, may have a symbolic meaning and may send a particular message about the product’s owner.

Media also propagate other noncommercial symbols. National and state flags, religious images, and celebrities gain shared symbolic meanings through their representation in the media.

Spiral of Silence

The spiral of silence theory, which states that those who hold a minority opinion silence themselves to prevent social isolation, explains the role of mass media in the formation and maintenance of dominant opinions. As minority opinions are silenced, the illusion of consensus grows, and so does social pressure to adopt the dominant position. This creates a self-propagating loop in which minority voices are reduced to a minimum and perceived popular opinion sides wholly with the majority opinion. For example, prior to and during World War II, many Germans opposed Adolf Hitler and his policies; however, they kept their opposition silent out of fear of isolation and stigma.

Because the media is one of the most important gauges of public opinion, this theory is often used to explain the interaction between media and public opinion. According to the spiral of silence theory, if the media propagates a particular opinion, then that opinion will effectively silence opposing opinions through an illusion of consensus. This theory relates especially to public polling and its use in the media (Papacharissi).

Media Logic

The media logic theory states that common media formats and styles serve as a means of perceiving the world. Today, the deep rooting of media in the cultural consciousness means that media consumers need engage for only a few moments with a particular television program to understand that it is a news show, a comedy, or a reality show. The pervasiveness of these formats means that our culture uses the style and content of these shows as ways to interpret reality. For example, think about a TV news program that frequently shows heated debates between opposing sides on public policy issues. This style of debate has become a template for handling disagreement to those who consistently watch this type of program.

Media logic affects institutions as well as individuals. The modern televangelist has evolved from the adoption of television-style promotion by religious figures, while the utilization of television in political campaigns has led candidates to consider their physical image as an important part of a campaign (Altheide & Snow, 1991).

Cultivation Analysis

The cultivation analysis theory states that heavy exposure to media causes individuals to develop an illusory perception of reality based on the most repetitive and consistent messages of a particular medium. This theory most commonly applies to analyses of television because of that medium’s uniquely pervasive, repetitive nature. Under this theory, someone who watches a great deal of television may form a picture of reality that does not correspond to actual life. Televised violent acts, whether those reported on news programs or portrayed on television dramas, for example, greatly outnumber violent acts that most people encounter in their daily lives. Thus, an individual who watches a great deal of television may come to view the world as more violent and dangerous than it actually is.

Cultivation analysis projects involve a number of different areas for research, such as the differences in perception between heavy and light users of media. To apply this theory, the media content that an individual normally watches must be analyzed for various types of messages. Then, researchers must consider the given media consumer’s cultural background of individuals to correctly determine other factors that are involved in his or her perception of reality. For example, the socially stabilizing influences of family and peer groups influence children’s television viewing and the way they process media messages. If an individual’s family or social life plays a major part in her life, the social messages that she receives from these groups may compete with the messages she receives from television.

Key Takeaways

• The now largely discredited direct effects model of media studies assumes that media audiences passively accept media messages and exhibit predictable reactions in response to those messages.
• Credible media theories generally do not give as much power to the media, such as the agenda-setting theory, or give a more active role to the media consumer, such as the uses and gratifications theory.
• Other theories focus on specific aspects of media influence, such as the spiral of silence theory’s focus on the power of the majority opinion or the symbolic interactionism theory’s exploration of shared cultural symbolism.
• Media logic and cultivation analysis theories deal with how media consumers’ perceptions of reality can be influenced by media messages.

Media theories have a variety of uses and applications. Research one of the following topics and its effect on culture. Examine the topic using at least two of the approaches discussed in this section. Then, write a one-page essay about the topic you’ve selected.

• Internet habits
• Television’s effect on attention span
• Advertising and self-image
• Racial stereotyping in film
• Many of the theories discussed in this section were developed decades ago. Identify how each of these theories can be used today? Do you think these theories are still relevant for modern mass media? Why?

David Altheide and Robert Snow, Media Worlds in the Postjournalism Era (New York: Walter de Gruyter, 1991), 9–11.

Dearing, James and Everett Rogers, Agenda-Setting (Thousand Oaks, CA: Sage, 1996), 4.

Hanson, Ralph. Mass Communication: Living in a Media World (Washington, DC: CQ Press, 2009), 80–81.

Hanson, Ralph. Mass Communication , 92.

Jansson-Boyd, Catherine. Consumer Psychology (New York: McGraw-Hill, 2010), 59–62.

Papacharissi, Zizi. “Uses and Gratifications,” 153–154.

Papacharissi, Zizi. “Uses and Gratifications,” in An Integrated Approach to Communication Theory and Research , ed. Don Stacks and Michael Salwen (New York: Routledge, 2009), 137.

Stille, Alexander. “Marshall McLuhan Is Back From the Dustbin of History; With the Internet, His Ideas Again Seem Ahead of Their Time,” New York Times , October 14, 2000, http://www.nytimes.com/2000/10/14/arts/marshall-mcluhan-back-dustbin-history-with-internet-his-ideas-again-seem-ahead.html .

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Article contents

The mass media and the policy process.

• Annelise Russell , Annelise Russell Department of Government, University of Texas at Austin
• Maraam Dwidar Maraam Dwidar Department of Government, University of Texas at Austin
•  and  Bryan D. Jones Bryan D. Jones Department of Government, University of Texas at Austin
• https://doi.org/10.1093/acrefore/9780190228637.013.240
• Published online: 31 August 2016

Scholars across politics and communication have wrangled with questions aimed at better understanding issue salience and attention. For media scholars, they found that mass attention across issues was a function the news media’s power to set the nation’s agenda by focusing attention on a few key public issues. Policy scholars often ignored the media’s role in their effort to understand how and why issues make it onto a limited political agenda. What we have is two disparate definitions describing, on the one hand, media effects on individuals’ issue priorities, and on the other, how the dynamics of attention perpetuate across the political system. We are left with two notions of agenda setting developed independently of one another to describe media and political systems that are anything but independent of one another.

The collective effects of the media on our formal institutions and the mass public are ripe for further, collaborative research. Communications scholars have long understood the agenda setting potential of the news media, but have neglected to extend that understanding beyond its effects on mass public. The link between public opinion and policy is “awesome” and scholarship would benefit from exploring the implications for policy, media, and public opinion.

Both policy and communication studies would benefit from a broadened perspective of media influence. Political communication should consider the role of the mass media beyond just the formation of public opinion. The media as an institution is not effectively captured in a linear model of information signaling because the public agenda cannot be complete without an understanding of the policymaking agenda and the role of political elites. And policy scholars can no longer describe policy process without considering the media as a source of disproportionate allocation of attention and information. The positive and negative feedback cycles that spark or stabilize the political system are intimately connected to policy frames and signals produced by the media.

• public policy
• public opinion
• agenda setting

By 1981 , the field of political communication had matured enough to warrant comprehensive treatment in a handbook (Nimmo & Sanders, 1981 ). The authors characterized the field as experiencing “a healthy, thriving, and burgeoning state” (p. 18). One is struck by the much broader set of topics captured by the 1981 handbook compared to this compilation, and the lack of emphasis on the mass media as communication systems in the earlier volume. But the 1981 volume was far more diffuse and suggestive of possible research directions compared to the coherent summaries of strong bodies of research that characterize the present volume. Today, the research is far more extensive; for example, the 1981 handbook included a single article, by Max McCombs, on the media and public opinion, whereas today there is a whole set of papers describing media effects on the public as well as a separate section on the effects of political campaigns. A similar contrast applies to the media-public policy nexus; the 1981 volume featured a single entry (by Cobb and Elder), while this collection includes five articles with a policy focus.

Cobb and Elder ( 1971 , pp. 408–414) centered a major part of their discussion on policy subsystems, distinguishing between communication essential to subsystem maintenance and incremental policymaking and that associated with major mobilizations and policy changes—those associated with the idea of agenda-setting (or agenda-building, as they termed it) so ably set out by Cobb and Elder elsewhere ( 1983 ). This dual role of the media in the policy process remains just as critical today.

Yet to date we have little research that addresses these systemic components of the relations between public policy and political communication, even given the emphasis that the topic receives in this volume. In this article, we explore this divergence, from each side of the divide, and point to potential unifying ideas for the future.

Although scholars of political science and communication have long studied agenda setting dynamics by exploring patterns in attention, there has been a distinct lack of connections between studies of the media and studies of public policy processes (Wolfe, Jones, & Baumgartner, 2013 ). In particular, “agenda setting” remains more or less a homonym between the two disciplines rather than a research topic with a common theoretical base.

Political scientists have studied agenda setting in the political system by exploring the formation and accessibility of the political agenda, as well as the causes of policy change and stability, often absent of a discussion of the media. Within political science, the public policy field’s examinations of the media provide a radically different perspective from that of communication scholars. Policy scholars posit that media attention—similar to policy attention—is episodic, providing high levels of attention to some issues, but ignoring most. Furthermore, studies indicate that the media is a major player in the policy cycle, inserting positive feedback (increased levels of coverage) and negative feedback (low levels of coverage, or no coverage) into the system, potentially corresponding with changes in the intensity of policymaking activity. This perspective argues in favor of pursuing studies of the relationship between the media and the policy process by focusing on exchanges of information between the two bodies, an information processing approach.

Communication scholars have generally focused on the impact of media coverage on the public agenda, that is, what the mass public believes, feels, and attends to. Scholars in this tradition have studied public opinion formation, evaluation, and engagement. They report finding that the media influences the national (public) agenda through its tendency to focus attention on a few key issues and thus determine the issues, and direction of the issues, that the public cares about (McCombs & Shaw, 1972 ). Scholars of political communication extend this inquiry to examine the extent to which the media’s agenda-setting capacity drives public attitudes, evaluations concerning the political system, and decision-making within the political process. This research focuses on the quantity and content of media coverage on public perceptions of television advertising in campaigns, candidate evaluations, vote choice, political engagement, and more, finding that the media is a key player in deciding what, and sometimes how, the public thinks about political issues and whether or not one will engage in the political process (Entman, 1989 ; Iyengar & Kinder, 1987 ; McCombs, 2004 ).

The independent tracks of these two approaches to agenda setting has led to the development of two disparate bodies of work and two separate definitions of agenda setting, distinct but relevant to one another. The communications literature on agenda setting has traditionally centered on the role of the media in setting the issue priorities of individuals and the mass public, while the policy field has focused on the dynamics of media issue attention and policymaking in the political system.

In this article, we argue in favor of integrating these two bodies of work that, for too long, have talked past one another. We argue that both policy and communication studies can benefit from a broadened, integrative approach toward studying media agenda setting. In doing so, we will begin by providing an overview of the literatures supporting both agenda setting perspectives, highlighting the dividing factors. Next, we will discuss the expanding body of work that has begun to integrate the two approaches to agenda setting in the media. Finally, we will propose four recommendations designed to aid policy and communication scholars in pursuing integrative approaches. These are our recommendations:

First and foremost, political communication scholars should consider the role of the media beyond its purported role as a linear driver of public opinion or policy. The news media’s effects arguably cannot be effectively modeled by linear relationships linking media effects, public opinion, and political activity. The public agenda cannot be comprehensively understood without a thorough understanding of the policymaking agenda and the role of political elites. Communication scholarship has previously neglected to extend the agenda setting capacity of the news media past its causal effect on the mass public—thus, we propose extending studies of the implications of media and public opinion to policymaking and the policy process.

Policy scholars studying policy change and attention allocation should address the role of the media as an institutional actor—often assumed in media studies—in the political system and assess the media as a key actor in the policy process.

The study of the relationship between the media and policy process would benefit from the use of an information processing perspective, characterized by exchanges of information uncovering policy problems, disseminating information, and potentially driving episodic policy change. A consideration of the media as a source of information supply—drawn from recent policy studies—can provide valuable insight for understanding feedback cycles and changes in attention across studies of both elite and mass politics.

Finally, a comparative or cross-national approach to studying media and the policy agenda has broad benefits. The symbiotic relationship between the media and the policy agenda is not a uniquely American experience, and the need for an integration of policy studies and political communication is not a uniquely American problem. A number of comparative policy scholars have begun to examine the relationship between the media, policy issue salience, public opinion, and governmental activity, finding significant relationships (Green-Pedersen & Stubager, 2010 ; Vliegenthart & Walgrave, 2008 , 2010 ). Future research efforts should not only continue addressing the links between media and the policy agenda, but should do so comprehensively, and comparatively.

The road to a more integrative approach to agenda setting is not clear nor void of hurdles and pitfalls, but we argue that it is worth exploring, as the potential for a greater understanding of the media within both mass publics and elite institutions is crucial. By combining communication and policy studies, we may be able to approach studies of the media from a more complex approach that explores the cyclical and dynamic nature of mass media influence.

Origins of Policy Agenda Setting

The policy tradition of agenda setting began in the mid-20th century as a rebuke of pluralist models that either ignored the accessibility of the political agenda or assumed a broad scope of influence for the agenda. E. E. Schattschneider ( 1960 ) highlighted the limited accessibility of political agendas, particularly in the context of conflict and power struggles. Schattschneider argued that the outcomes of political conflict are highly dependent on the scope of the surrounding conflict, which is determined, in part, by the number of the political players and the amount of competition involved ( 1960 ). Peter Bachrach and Morton S. Baratz ( 1962 ) critiqued the standard conceptions of power in the social sciences, arguing that there are two faces of power, one concerning the exercise of power, and one concerning the influences used to limit the scope of conflict or prevent conflict from occurring entirely ( 1962 ). Roger Cobb and Charles Elder ( 1971 ) highlight the difference between the systemic, public agenda (comprised of issues of high salience for the general public) and the institutional agenda (comprised of issues of high salience for government institutions), and propelled scholars to focus on how the agenda is formed. They proposed three steps to agenda formation: issue creation, issue expansion, and agenda entrance. In the first step, an issue is created as a result of activism by an initiator, in tandem with focusing events that provide the issue with staying power. The issue expands as it garners resources, attention, and mobilizes supporters—all serving to expand the scope of the conflict. Once the issue has expanded to garner high levels of awareness and interest from the mass public, it enters the agenda. Cobb and Elder further support this argument by detailing dimensions of issues that are fundamental to placement on the agenda, including specificity, social significance, temporal relevance, complexity, and historical precedence ( 1972 ). As we noted above, Cobb and Elder were the first policy scholars to recognize a key role for the media in the policy process. They depicted a dual role in the policy process for the media—subsystem reinforcement and major mobilizations.

Providing a foundation for the study of policymaking in a limited agenda space, Michael D. Cohen, James G. March, and Johan P. Olsen laid out a study of organizations and organizational problem solving (Cohen, March, & Olsen, 1972 ). They developed the now well-known “garbage can model of organizational choice,” in which organizations are characterized by a collection of decision-makers, problems, solutions, and opportunities, which flow in and out of the organization’s attention span. The theory posits that decision makers are constantly searching for opportunities to utilize pre-scripted solutions, issues are searching for opportunities to cause them to rise to the forefront, while solutions are searching for issues to address (Cohen, March, & Olsen, 1972 ). In this model, the authors argued that the pairing of problems and solutions in the garbage can occurs mainly due to chance (Cohen, March, & Olsen, 1972 ).

John Kingdon ( 1984 ) extended the Cohen, March, and Olsen ( 1972 ) study of organizational decision making to policymaking and the policy agenda, defining the policy agenda as a list of problems which government officials, and those associated with government, are paying attention” (Kingdon, 1984 ). Kingdon ( 1984 ) argued that policy change occurs as a function of attention and the simultaneous coupling of problems and solutions, and suggested a number of processes by which issues arise on the policy agenda. In particular, he introduced the concept of a “policy window,” a window of opportunity for policymaking that opens when a new problem (or problem definition) arises. In this window, a new solution to a problem may be developed (or a previously concocted solution recycled), and policy change is implemented (Kingdon, 1984 ). Kingdon envisioned a relatively limited role for the mass media in the policy process, which may have been a side consequence of his reliance on intensive interviews with highly placed policymakers who are more prone to attribute policy making to the goals and motives of individual decision makers than properties of the policy context including media attention.

Micro-Foundations and Macro-Effects in Agenda-Setting in Policy Studies

Key to all modern work on agenda setting is the notion of collective attention, based on Herbert Simon’s classic concept of bounded rationality as applied to the policy making process (Baumgartner & Jones, 1993 ; Jones, 2001 ). Simon originally proposed bounded rationality as a criticism of rational choice models of decision making and argued instead that decisions makers are bound by limited cognitive architectures and unknown factors that impact the decision-making process.

With bounded rationality as a micro-foundation for understanding the causes of policy change and stability, recent studies of policy agenda setting have focused on the roles of attention, information, and feedback in the policy cycle. This work began by introducing the concept of punctuated equilibrium to the social sciences, the notion that policymaking activity is characterized by long stretches of stasis and incremental change, followed by punctuations in policy change, and expands to control for the role of attention in problem prioritization processes in government (Baumgartner & Jones, 1993 ). Applying punctuated equilibrium theory to a policy context, Baumgartner and Jones illustrate that incremental, or stable, policy change is reinforced by a lack of government attention to an issue, while large-scale policy change is associated with heightened government attention to an issue ( 1993 ). They also attribute episodic policy change to shifts in framing, venues, and levels of mobilization surrounding the issue (Baumgartner & Jones, 1993 , Baumgartner & de Boef, 2008 ). In their study of major US laws passed between 1947 and 1998 , Baumgartner and Jones investigated the informational bases of decision-making, specifically exploring how policymakers interpret, manage, and respond to information ( 2005 ). They showed that major policy change is significantly related to how policymakers—and the political system as a whole—process information ( 2005 ). In an extension of this argument, Baumgartner and Jones ( 2015 ) further explored the role of information and information search processes in government, finding that limited information search processes lead to declines in policymaking, while extensive information search processes are closely related to surges in policymaking activity (Baumgartner & Jones, 2015 ).

The media has only recently been integrated into agenda setting studies as a major source of information, and as an integral institution in the political system. Writing in 2006 , Bartholomew Sparrow noted that policy scholars typically fail to consider the potential role of the media. He went on to propose an agenda for research, suggesting that scholars should consider the media’s role as a public diplomat, its patterns of interpreting, normalizing, and disseminating political information, potential disconnects between political issues as presented by the media and as understood by the public, and how the media is able to maintain its impact (relationships with lobbying groups, key legislators, FCC, and the judiciary), both inside and outside of the United States. Sparrow’s contribution was to highlight a gap in the literature that continues to persist—scholars have been slow to consider the role of uncertainties facing the media in the policy process, and until recently, few had explored the role of the media as a wide-reaching political institution (Van Aelst & Walgrave, 2016 ).

In one of these studies incorporating the news media into the policy process, Michelle Wolfe ( 2012 ) examines the relationship between media attention and the speed of policymaking. Wolfe characterizes the media as a “gatekeeper to arguments and interests,” capable of conditioning the speed of policymaking. She argues that the time it takes a bill—once introduced—to become a law increases as media coverage associated with the debate surrounding the bill reaches higher levels. Her findings indicated that increased media coverage slows down the speed of policymaking, as it causes new information, participants, and problem definitions to enter the debate, allowing for counter-mobilization by a bill’s opponents. The dynamic nature of media effects on feedback cycles is further explored in a study of front-page articles in the New York Times from 1996 to 2006 , where Amber Boydstun ( 2013 ) examines the process by which policy issues make it onto the media agenda, uncovering long-standing patterns in coverage. She finds that, by and large, most policy issues receive little to no media coverage, while a few issues receive explosive levels of coverage. She attributes this to positive feedback effects within the media, in which coverage begets coverage, rather than being prompted directly by the scope and duration of the underlying event. This type of work, exploring the role of feedback into the policy system, is necessary to further understand and fully appreciate the media’s role in the policy process.

Origins of Media Agenda Setting

Studies of the media agenda can be traced back to Walter Lippman’s ( 1922 ) observation that the news media filter reality and provide the “pictures in our minds” concerning the course of public affairs and current events. While, for much of the 20th century, communications scholars operated under a “limited effects” media model, arguing in favor of the media’s inability to affect public perceptions, scholars soon came to realize that the priorities of the news media strongly shaped those of the mass public. This literature argues not only that the media influences public opinion, but also that the media also has the capacity to influence the direction of public opinion.

In their seminal study of media agenda setting, McCombs and Shaw ( 1972 ) compared the level of issue-related newspaper coverage in Chapel Hill, North Carolina with public responses to Gallup’s question about the most important problem facing the country. In effect, McCombs and Shaw explored the early stages of opinion formation and information acquisition, finding that the public’s perception of the most important problem facing the country closely reflected the patterns of issue coverage in newspapers. This finding launched media agenda setting research and many subsequent studies have confirmed the hypothesis that the media has the capacity to set the public agenda (Funkhouser, 1973 ; Shaw & McCombs, 1977 ; Weaver, Graber, McCombs, & Eyal, 1981 ; Winter & Eyal, 1981 ). Furthermore, comparative scholars of the media have found similar effects across the world (McCombs, 2004 ).

This work on agenda setting effects has been organized into studies of agenda setting, priming, and framing. Here, the literature posits that media emphases of issues and objects drive the topics that the public thinks about (Wu & Coleman, 2009 ). Complimentary agenda-setting studies focus on the attributes of issues or how they are framed in the media (Ghanem, 1997 ; McCombs, 2004 ). This work highlights the media’s ability to draw attention to certain characteristics of issues in the news—the focus is not on what the news media emphasize, but how they describe their issues of emphasis, thus focusing public attention on certain attributes or frames (Coleman & Banning, 2006 ; Ghanem, 1996 ; McCombs, Lopez-Escobar, & Llamas, 2000 ). Furthermore, this line of work argues that the tone of an issue is just as important as the amount of coverage that an issue receives (Coleman & Banning, 2006 ).

Stemming from this extensive documentation of the media’s agenda setting effects, scholars of political communication have explored the effects of framing and priming by studying if, how, and why media agenda setting can drive public attitudes and engagement in the policy process. Studies of campaigns, candidate evaluations, vote choice, and political engagement find that media framing and priming are key players in deciding what, and how, the public thinks about political issues, as well as key drivers of public engagement in the political process (Entman, 1989 ; Iyengar & Kinder, 1987 ; McCombs, 2004 ). In an analysis of national television news, Shanto Iyengar and Donald Kinder ( 1987 ) find that issues in the news are weighed more strongly when the public evaluates their political leaders. In this way, television news coverage has a strong effect on public opinion, without changing underlying attitudes (Iyengar & Kinder, 1987 ). In a later update of this study, the authors find that the issues that receive the most attention in national television news become the most important issues to viewers, while those issues that do not receive attention in national television news are not of the highest importance to viewers (Iyengar & Kinder, 1987 ). Similarly, Robert Entman ( 1989 ) examined political messages in newspapers, finding a significant relationship between the content of these messages and the political attitudes of readers. While these studies have clearly established a link between the media and the public agenda, and have suggested that issue salience—as driven by media framing and priming—may affect vote choice, they have often lacked a clear reference to how these mechanisms drive change in the policy process. Thus, we may argue that the implications of the media’s direct and indirect impacts on the political process have yet to be fully integrated into agenda setting studies.

Integrative Approaches to Agenda Setting

While the political communication literature has extensively explored public agenda formation in the context of political messages, evaluations, and behavior, much of this literature has stopped short of linking these findings to the broader policy process. Before delving into our recommendations for further integrative work, it important to overview current and past scholarship that has probed the links between media and the policy system.

Bryan Jones and Michelle Wolfe ( 2010 ) study the media’s patterns of receiving and prioritizing information, extending the scholarship from discussions of public agenda setting. Jones and Wolfe’s work provides support for an indexing hypothesis of the relationship between the news media and politicians: this hypothesis argues that debates in the formal political system set the agenda for debates in the media (Jones & Wolfe, 2010 ).

Frank Baumgartner and Suzanna De Boef ( 2008 ) and Amber Boydstun ( 2013 ) connect the media’s tendency to frame issues from a particular perspective with changes in policy over time. They demonstrate that media framing of the death penalty has a substantial impact on changes in capital punishment policy over time. Along the same lines, Rose and Baumgartner ( 2013 ) examine the impact of framing the poor on federal funding of social programs, finding significant links between shifting frames of the poor and federal social welfare spending. Eric Jenner ( 2012 ) deviates from the standard analyses of news articles to examine the influence of news photographs, focusing on media coverage of environmental news. Jenner argues that photogrpahic attention to environmental issues in the media influences issue salience for the mass public and elite actors. He examines public opinion polls, environmental news stories in The New York Times , and environmental news photographs in Time magazine. He finds that news photographs—unlike news articles—have a significant impact on congressional committee attention, but have little impact on public opinion (Jenner, 2012 ).

Integrative approaches to agenda setting also extend to comparative spheres: Soroka and Lim’s ( 2003 ) study of media coverage, public opinion, and foreign policy across the United States and the United Kingdom highlighted a strong correlation between foreign affairs issue salience in the media, and in the public mind. Following the previous literature’s suggestion that governments react to issue salience, Soroka and Lim posit that issue salience may drive defense spending in the United States and United Kingdom, since changes in spending may be a reaction to foreign affairs issue salience. Peter Van Aelst and Stefaan Walgrave ( 2011 ) survey Members of Parliament in Belgium, Netherlands, Sweden, and Denmark, questioning them on their perceptions of the media’s power as an agenda setter in the political system. Their findings indicate that the majority of MPs consider the media to play a very important—if not the most important—role as an agenda setter in their political systems.

These studies suggest that the media has an unquestionable impact on the policymaking process. But, importantly, policymakers try to influence the media as well. Lance Bennett ( 1990 ) developed the indexing hypothesis, positing that journalistic norms constrain news coverage by indexing coverage to what policymakers are saying about an issue being covered. While the thesis has been controversial, the thesis was groundbreaking in its attention to government’s effect on media, hence reversing the standard direction of causation of media influence on policymakers, and integrated journalistic norms into the process. Fifteen years later, Bennett, Lawrence, and Livingston ( 2006 ) re-examined the thesis to try to see when the media might develop alternate narratives based on other sources.

Moving Forward: Bridging the Media and Policy Divide

Media as an institution within the policy process.

Building on recent approaches that have begun to integrate media and policy studies, we argue that researchers must consider the role of the media as a political institution in studies of the political system, public opinion and policy process. Political institutions have norms that shape daily interactions with the policy process, and media outlets are one of many policy actors whose routines and organization lead to a regular presence in the political system. The media’s organizational process is characterized by institutional incentives, such as attending to elite actor and consumer demands (Boydstun, 2013 ). Just as daily subsystem interactions may affect the policy process, so too do the daily decisions that occur within the newsroom. Media scholars have often studied the role of newsroom interactions and institutional norms on the production of news and its impact on citizens, but these institutional patterns have lasting effects for the policy process that remain largely unknown. Meanwhile, the media is increasingly recognized as an integral part of the feedback systems that characterize the policy process (Boydstun, 2013 ; Wolfe, 2012 ), but meso- and micro-level studies of the effects of journalistic norms and practices require additional attention. For instance, is the shift toward a more professionalized media and the responding growth of communications staff within Congress a critical factor in the types of issues that make it onto the policy agenda? Or do they impact the nature of those issues, as in the speed, timing, and context surrounding proposed measures? Shifts in digital media—a 24-hour news cycle, the Internet, blogs, and social media—have all changed the way politicians interact with the media and the public, but what does this shift mean for the policy process? A closer link between the routines of journalists and their role as a political institution must be integrated with studies of elite policy actors and their relationship to the policy process as a whole.

Media as a Mechanism for Disproportionate Information

Even more important than understanding the media as a formal institution within the policy process, we should begin to understand the effects of media actors’ interaction within that process. The media serves as part of a complex information-processing system that influences the public policy process throughout (Jones & Wolfe, 2010 ). Scholars have argued that the political preferences of journalists, economic pressures, and industry standards are major factors in the process of determining the quantity and content of news coverage. Since the quantity and content of news coverage have significant implications for the public policy process, these factors deserve extensive study when considering the role of the media as a potential disseminator of disproportionate information.

Despite standards of unbiased reporting, the political attitudes of journalists and editorial decision makers may be a major source of bias affecting the topics covered by the news (Hackett, 1984 ). Researchers have explored this relationship domestically and comparatively, finding a significant correlation between journalists’ personal beliefs and their respective news decisions in the United States, Great Britain, Germany, Italy, and Sweden. While this relationship is more significant among newspaper journalists than broadcast journalists, partisanship maintains a modest impact across all news decisions (Patterson & Donsbagh, 1996 ). Some studies have suggested that media coverage in the United States is characterized by a liberal bias; for example, Groseclose and Milyo study partisan bias in the media by comparing media citations of think tanks and other policy groups to the citations of similar think tanks and policy groups by Members of Congress, finding a strong liberal bias (Groseclose & Milyo, 2005 ).

The patterns of news generation by journalists and editors combine to produce both positive and negative feedback cycles that characterize how and when elite attention is allocated among issues (Boydstun, 2013 ). Negative feedback is the process by which changes in the political system are “corrected” or “countered” by an opposing shift back toward equilibrium. In the context of news generation, negative feedback is produced by daily or routine media coverage that maintains the current allocation of attention across issues and the type of frames used to present the issue. Positive feedback mechanisms reinforce changes that may rapidly alter the political agenda, replacing the current policy image or definition with a completely new frame. The media can often supply momentum, and this shapes the policy agenda through positive feedback forces (Boydstun, 2013 ).

The balance between feedback cycles produces media outputs that are often skewed or disproportionate, such that over time some issues receive a dominant amount of media attention while others receive almost none. For instance, a surge of media coverage may follow a highly publicized event—such as Hurricane Katrina—but this positive feedback then limits or curbs the attention of simultaneously occurring events or issues—a negative feedback effect. These skews in attention are the result of a disproportionate information processing system, meaning that agendas do not reflect events in real time or in proportion to the relative magnitude of those events. For policymakers, this disproportionate system holds many implications for policy actors’ efforts to sufficiently and substantively respond to policy problems. This means that the issues that policymakers are often compelled to address are likely a function of skewed media coverage. Elite actors are already part of a disproportionate information process in which limited attention and processing power lead to episodic shifts in policy. The media’s contribution to positive and negative feedback cycles only add to the complexity of how institutions and actors operate within an episodic and disproportionate policy process.

Comparative Approach to Policy and Media

Agenda setting in studies of public policy and the media has become much more frequent over the last 20 years, as the underlying foundations of both theories have been found common across multiple political and media systems. Media agenda setting has examined the effects of agenda setting on public opinion and attitude formation in multiple comparative assessments.

Scholars found public opinion toward the final British governor in Hong Kong to be closely tied to the content of news coverage—through weekly tracking polls, they found that the news media often primed the public’s reaction (Willnat & Zhu, 1996 ). A study of media influence in Israel examined agenda building, agenda setting, and priming in the context of four Israeli elections, finding that media coverage has significant priming effects on the voting intentions of individuals and aggregate election results (Sheafer & Weimann, 2005 ). Similarly, a study of news coverage of a national referendum campaign in Denmark (concerning the introduction of the euro) studied the impact of news coverage of the campaign on public evaluations of political leaders. Here, findings suggest as the issue of the introduction of the euro became more visible in the media, it became more important for shaping evaluations of the incumbent government, prime minister, and opposition leaders (de Vreese, 2004 ), supporting the priming hypothesis. In studying media coverage of Spanish elections, scholars found a positive relationship between the media’s effective attribute agenda about the candidates leading up to the election and the voters’ attribute agendas about the competing candidates (Canel, Llamas, & Rey-Lennon, 1996 ). What is missing from these studies, however, is a discussion of how the media’s effects feed back into the political system. This missing link is where media agenda setting ends and policy agendas begin.

The study of policy agenda setting has benefited from the establishment of the Comparative Agendas Project, which aggregates agenda setting measures across political systems and enables cross-system analyses of global policy. International scholars have been at the forefront of integrating media and policy studies by looking at how the media affects the policy agenda, especially the legislative process. Rens Vliegenthart and Stefaan Walgrave attempt to operationalize both notions of agenda setting by identifying the disparity between media and policy agendas (Vliegenthart & Walgrave, 2011 ). They examine media coverage as an indicator of attention and construct a model to assess how political parties and legislative action contour the media’s agenda setting influence. They conclude that the media has a considerable effect on the policy agenda, and that this effect is greater for opposition parties and smaller parties who are more reliant on journalists to get their message across. Another study by Walgrave and Peter Van Aelst offers a comparative perspective that finds media effects on agenda setting in general, though the authors argue for a more dynamic analysis of the relationship between media and policy (Walgrave & Van Aelst, 2011 ).

It is that call for a dynamic analysis between not only media and policy, but media, policy, and the public, that we echo. Media agenda setting often begins and ends with issue salience in the mass public, and policy scholars refrain from discussing the public implications of media influence on policy. Comparative analysis is a venue for bridging this gap as both the communication and policy fields further broaden the applicability of agenda setting beyond the United States.

Moving Beyond Linear Assumptions

The influence of the media agenda on public opinion is often represented through simple linear models and correlations that illustrate a casual or direct link between the media’s message frame and what the public believes to be salient. This assumes a very hierarchical structure in the sense that the media distributes a message that the public subsequently receives, according to the media’s ability to prime and frame the issue. Scholars have used this linear structure to test the content of news stories, the tone or attributes of those stories, and more networked approaches that combine both substance and tone of the articles or broadcasts. While the measures of content are further explored, all too often the assumption about the senders and receivers remains the same.

Media scholars struggle with a limited frame about the relationship of media and the public, while policy scholars continue to tangle with the notion of how the media fits into an “untidy” process (Kingdon, 1984 ). The media can have effects on the policy process as a mechanism for both positive and negative feedback, but it is also a recipient of the outputs of these political processes. Few scholars attempt to grapple with this endogeneity problem, preferring to posit a directional causal arrow from the media to the policy process with few implications beyond that as far as the eventual repercussions for the media, the public, or policy makers.

Both media and policy agenda setting studies stand to benefit from analyses that pull one another away from their corners and embrace the dynamic nature of agenda setting, where effects are not just within elite institutions or the public, but rather part of one larger process. We must consider the media’s agenda setting role, not only as a primer for public salience but the effects that has on the issues that politicians take up in Congress or Parliament. The actions of elite officials do not end with the passage of legislation, but have reverberations that extend beyond elite institutions to the public. The media is one connection between elite decisions and public perception, and is able to transfer issue salience from one public to another. Agenda-setting studies cannot and should not move forward without a better consideration for the entire political process rather than one-to-one linear relationships.

Studies of agenda setting have become increasingly common and more complex as scholars across disciplines attempt to better understand the role of the media. We have argued that the nature of this complexity is too often confined to either elite notions of media influence (policy) or mass public effects (communications), and that agenda-setting studies must begin to look at the relationships both elite and mass publics foster with the media within the agenda-setting process. Instead of relying on small-scale case studies, an integrated approach also enables more complex and collaborative database analysis. New technology enables research that breaks traditional discipline norms, and we must take advantage in the research moving forward.

We have proposed four avenues for better integrating policy and communication studies in ways that bridge the divide between what have historically been two completely separate research agendas. Many scholars studying political processes acknowledge a role for the media in the agenda setting process, but a better understanding of that role should include considerations of the media as a political institution, a disproportionate processor of information within a system that provides information to elite and mass publics, has a comparative studies advantage, and as a bridge between elite and public priorities. The basic principles of agenda setting, priming, framing, and issue definition are ever present across research, and we must take advantage of what is similar and useful to our overall better understanding of media’s agenda-setting influence. For too long, two richly diverse and complex bodies of work have talked past one another, and policy and communication studies can benefit from a broadened, integrative approach toward studying media agenda setting.

Acknowledgements

We appreciated the comments from Shanto Iyengar and Stefan Walgrave.

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• Published: 05 September 2023

Mass media impact on opinion evolution in biased digital environments: a bounded confidence model

• Valentina Pansanella 1 , 4 ,
• Alina Sîrbu 2 ,
• Janos Kertesz 3 &
• Giulio Rossetti 4  

Scientific Reports volume  13 , Article number:  14600 ( 2023 ) Cite this article

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• Complex networks
• Computational science
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People increasingly shape their opinions by accessing and discussing content shared on social networking websites. These platforms contain a mixture of other users’ shared opinions and content from mainstream media sources. While online social networks have fostered information access and diffusion, they also represent optimal environments for the proliferation of polluted information and contents, which are argued to be among the co-causes of polarization/radicalization phenomena. Moreover, recommendation algorithms - intended to enhance platform usage - likely augment such phenomena, generating the so-called Algorithmic Bias . In this work, we study the effects of the combination of social influence and mass media influence on the dynamics of opinion evolution in a biased online environment, using a recent bounded confidence opinion dynamics model with algorithmic bias as a baseline and adding the possibility to interact with one or more media outlets, modeled as stubborn agents. We analyzed four different media landscapes and found that an open-minded population is more easily manipulated by external propaganda - moderate or extremist - while remaining undecided in a more balanced information environment. By reinforcing users’ biases, recommender systems appear to help avoid the complete manipulation of the population by external propaganda.

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Introduction.

Opinions and beliefs shape individual behavior, which drives human actions, and a society’s collective behavior, influencing politics, public health, and the environment. Changes in public opinion - even the formation of committed minorities - may profoundly affect decision-making and politics: a recent example is the temporary suspension of the Oxford-AstraZeneca vaccine during March 2021 1 , which has cost a slowdown in the vaccination strategy and had direct consequences on public health. Social interactions 2 are the main ingredient driving the opinion evolution process. According to social influence theory 3 , an interaction between social agents typically reduces the difference between their opinions or, at worst, leaves it unchanged. Besides social influence, opinion formation also depends on the information people collect from external sources (mainly in the form of mass media broadcasts), enhancing awareness of socio-political issues and events 4 , 5 . For instance, traditional mass media have been argued to influence individual and public health 6 , 7 on issues ranging from eating disorders 8 , tobacco consumption 9 , and vaccinations 10 . Moreover, news articles, TV news, and political talk shows all play a central role in shaping opinions, especially when it comes to the communication of political information, which is a key process in the political system, arguably holding the power to manipulate how people think about internal and international politics.

However, media coverage often exhibits an internal bias, reflected in the news and commonly referred to as media bias 11 . Factors influencing this bias include ownership or a specific political or ideological stance of the outlet and its target audience 12 . Media choices can also be influenced by their profit-oriented nature, leading to content selection aligned with the audience’s interests that fuels this profit, disregarding issues and problems (and portions of the population, such as minorities) that would guarantee fewer earnings 13 .

As theoretical studies show, reading news or being the target of mass political propaganda 14 , 15 may affect our belief system. External agents (i.e. a government, a company, or a group of terrorists) may be interested in actively shifting the public’s opinion concerning a specific topic. Propaganda can be exploited to try and promote one opinion over the others 16 , to achieve a certain value for the consensus opinion 17 , or even to prevent people from reaching more extreme opinions 18 . However, when agents have different opinions, a single aggressive media may, in reality, produces an undesired result 19 : an antagonist cluster at the opposite extreme of the opinion spectrum.

Besides the information social agents can access, and how this information is presented to them, a series of internal mechanisms play an important role in shaping opinions and beliefs. The way people process information is, in fact, far from being perfectly rational and is highly influenced by psychological factors and cognitive biases 20 . Psychological studies 21 , 22 have observed that people, both online and offline, feel discomfort when encountering opinions and ideas that contradict their existing beliefs, i.e. experience cognitive dissonance 23 . Such cognitive biases have often been studied through models of bounded confidence 24 , i.e. the tendency to ignore beliefs that are too far from our current ones, or mimicking the backfire effects 25 , i.e. the tendency to reject countering evidence and to strengthen the support to the current belief. When considering cognitive biases, extremist propaganda may become efficient when the message is promoted with a certain frequency 26 . When the propaganda is made on more moderate stances or when the population is more open-minded, the population conforms to the propaganda if the message is delivered frequently enough. When the media landscape is heterogeneous 27 , media outlets can employ different strategies to maximize their audience. For instance, on some issues of general interest, each media outlet tries to imitate successful behaviors (e.g. promoting closer opinions to the most followed media). On other more ideologically charged issues, media outlets may compete (i.e. disagreeing with the other media), promoting thus opinion fragmentation in the population. The presence of repulsive behaviors 28 suggests that propaganda can drive the population to form a consensus around an external message, regardless of whether the message is extreme or moderate: as a result of wanting to be apart, agents end up together sharing the same opinion.

While such a dynamic has always existed, how people retrieve information has profoundly changed in the last twenty years. Television remains the most common media source among Europeans 29 , but the use of the Internet and online social networks (OSNs) is steadily rising alongside the decline of the readership of newspapers. However, OSNs are also environments where individuals express their opinions, discuss, and share content from other sources. These environments are ruled by algorithms that filter and personalize each user’s experience accordingly to their and their friends’ past behavior. This is intended to maximize users’ engagement and enhance platform usage, however it is theorized that filtering algorithms and recommender systems are likely to create an algorithmic bias 30 . By showing people only narratives aligned with their own existing beliefs, a positive feedback loop is obtained, reducing the amount of diversity in the user experience, contributing to the creation and maintenance of echo chambers 31 and filter bubbles 32 , 33 , 34 . Although personalization is essential in information-rich environments (to allow people to find what they are looking for and increase user engagement), there is great concern about the negative consequences of algorithmic filtering. Therefore, understanding how mass media information impacts public opinion and how cognitive and algorithmic biases play a role in social influence mechanisms is essential to enrich our understanding of human behavior and also to define mitigation strategies to avoid unintended consequences.

In this paper, we approach such a goal through the lens of opinion dynamics models 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , a field of study born within the statistical physics area which is now mainly studied through the lens of computational social sciences. Indeed, the possible effects of mass media have been widely investigated through such models 14 , 15 , 16 , 17 , 18 , 18 , 26 , 27 , 43 , 44 , 45 , 46 , 47 . However, to the best of ourknowledge, none of these works combine the role of online platforms and algorithmic biases with different possible media landscapes. The present work aims to analyze the effects of different mass media landscapes - ranging from extremist propaganda to a more balanced opinion diet - in a synthetic environment, simulating a general OSN where agents can interact with each other, but interactions are always mediated by a recommender system, selecting content aligned with agent beliefs. To investigate the role of mass media in shaping public opinion, we extended the Algorithmic Bias model 30 (which, in turn, extends the Deffuant-Weisbuch one 24 ), adding the possibility to specify a number of external mass media agents, defining the opinions they promote, and the frequency of agent-media interactions. We conducted numerical simulations to examine this extended model and analyzed the outcomes within the context of mean-field scenarios. Furthermore, we present a case study on a real-world network, illustrating how this model effectively captures a behavior that the baseline model fails to capture.

The present work aims to extend the Algorithmic Bias model 30 to understand how interacting with mass media in a biased environment (i.e. ruled by recommender systems and filtering algorithms) influence the outcome of the opinion evolution. In our simulations, we consider 100 agents with continuous opinions in the interval [0, 1], which can model opinions on any issue, with values 0 and 1 representing the most extreme opinions. The agents are allowed to interact with each other at discrete time intervals and with a fixed number of M stubborn agents, representing traditional media outlets that promote a fixed opinion over the whole time period. To represent this environment realistically, interactions (agent-to-agent and media-to-agent) are subject to cognitive and algorithmic biases. The stronger the algorithmic bias, \(\gamma\) , the higher the probability of interacting with similar agents and the lower the probability of interacting with different ones. Cognitive bias - specifically bounded confidence - limits interaction to an agent’s opinion neighborhood: two agents influence each other (according to social influence theory, adopting their mean opinion) if and only if their initial opinion distance is below a certain threshold \(\epsilon\) . This parameter is constant across the whole population and over time. In the reminder of the present work we often refer to it as the level of “open-mindedness” of the population because bounded confidence and open-mindedness both involve a willingness to consider different perspectives within certain limits. On the other hand, influenciability refers to being easily swayed by others, regardless of the strength of their arguments. Thus, we felt that open-mindedness was a more appropriate term for describing the bounded confidence threshold in our paper (for example as in 48 ). However, it’s important to note that in opinion dynamics models, behavioral and psychological factors are often simplified and represented by model parameters. As a result, nuances can be lost and the bounded confidence threshold could also be interpreted as influenciability. To control the frequency of interactions with the media, we set a fixed probability \(p_m\) - constant over time and across the whole population - which defines how likely it is to interact with a news piece (stubborn agent) after a user-to-user interaction. In our experiments, we assumed a mean-field context (e.g. all individuals can interact with all other agents without any social restrictions), which is a good starting point for analyzing the behavior of an opinion dynamics model. The model is detailed in Sect. " Model and methods ".

The scenarios we analyzed in the present work are (i) a single moderate media ( \(x_m = 0.5\) ), to discover whether a “moderate message” would prevent the population from polarising in cases where it would happen without propaganda; (ii) extremist propaganda, where there is only one news source promoting a fixed extreme opinion (in this case, it was set to \(x_m = 0.0\) , but the same conclusions hold for 1.0); (iii) two polarised media sources, promoting two opinions at the opposite sides of the opinion spectrum ( \(x_{m1} = 0.05 \text { and } x_{m2} = 0.95\) ); (iv) finally, we also investigated a more balanced scenario where there are two polarised media sources (same as above) and a moderate one (promoting the central opinion of the spectrum, i.e. \(x_{m3}=0.5\) ).

Without external effects, the population tends to: (i) polarise around moderately extreme positions (i.e. 0.2 and 0.8) when agents are “close-minded” ( \(\epsilon \le 0.32\) ); (ii) reach consensus around the mean opinion (i.e. 0.5) when agents are “open-minded” ( \(\epsilon > 0.32\) ), while the recommender system increases polarization/fragmentation, as shown in 30 .

In the remainder of this section, we analyzed these four different media landscapes and their effects on the opinion dynamics compared to the baseline model 30 .

A moderate media in a biased environment favors the emergence of extremist minorities

Average number of clusters in the moderate setting. In the figure, the average number of clusters of the final opinion distribution is represented as a function of the algorithmic bias \(\gamma\) and the probability of user-media interaction \(p_m\) for different bounded confidence values \(\epsilon\) . Values are averaged on 100 independent runs of each setting.

Average percentage of agents in the media cluster (0.5) in the moderate setting. In the figure, the average percentage of agents in the moderate cluster (0.5 +– 0.01) of the final opinion distribution is represented as a function of the algorithmic bias \(\gamma\) and the probability of user-media interaction \(p_m\) for different bounded confidence values \(\epsilon\) . Values are averaged on 100 independent runs of each setting.

In the first setting, we analyzed the effects of a “moderate message” on the opinion formation process, i.e. a single mass media promoting a central opinion ( \(x_m = 0.5\) ). We start from the hypothesis that such a media landscape may counteract the polarizing effects of a low bounded confidence \(\epsilon\) or the fragmenting effects of a high algorithmic bias \(\gamma\) . Bounded confidence, as in the baseline model, can be so high that all agents are eventually drawn towards the same opinion (regardless of the strength of algorithmic bias), as in the case of \(\epsilon =0.5\) (Fig. 1 d). In general, in this setting, both cognitive and algorithmic biases maintain the effects they have in the baseline model: a higher confidence bound is more likely to push the population towards consensus, while a higher algorithmic bias increases the level of fragmentation in the final opinion distribution.

What emerged from our simulations is that, when interactions are not mediated by the recommender system ( \(\gamma =0\) ), fragmentation increases with the frequency of agent-to-media interactions: in fact, the average number of opinion clusters at equilibrium (see Fig. 1 ) increases with ( \(p_m\) ). Such tendency is due to the fact that, by increasing \(p_m\) , the portion of the population which initially has the media within their confidence bound moves towards such opinion faster than in the baseline model , thus reducing the probability of attracting agents at a distance greater than \(\epsilon\) from the media that, in turn, will eventually stabilize around more extreme positions. When the social dynamic is, instead, mediated by a filtering algorithm, biasing the choice of the interacting partner towards like-minded individuals, the level of opinion fragmentation in the population is initially lower (for small \(p_m\) ) with respect to the baseline model ( \(p_m=0.0\) ), but - likewise - it grows as agent-to-media interactions become more frequent. These results disprove our initial hypothesis that a “moderate” propaganda may straightforwardly counter polarization/fragmentation. Instead, promoting a single “moderate” opinion may not push the population to conform towards the desired point of view. Fragmentation is reduced only when the frequency of interaction with media is low. Otherwise, it also becomes a fragmenting factor.

Besides the number of clusters that coexist in the stable state, if we look at the whole opinion evolution process, we can see that there is always a portion of the population clustering around the media opinion (i.e. with opinion \(x_i \in [0.5 +/- 0.01]\) , while a small fraction assumes extremist positions. Figure 2 shows this cluster’s population percentage. The more open-minded the population and the higher the frequency of agent-to-media interactions, the larger the portion of agents that the media can rapidly attract towards the average opinion: thus, pushing the population towards consensus and countering the slowing down effect created by the algorithmic bias. Moreover, as we can see from Fig. 2 , while in the baseline model, only a narrow portion of the population assumes the mean opinion, when a moderate media is promoting that opinion, we can see that the portion of the population ending in the moderate cluster in the steady state grows even with just a low probability to interact with the media and narrow open-mindedness threshold. Therefore, while consensus is not fully reached, a major cluster around the media is observed. Conversely, in the case of media absence ( \(p_m=0.0\) ), there is a higher variability in the final size of the moderate cluster. Even when a consensus forms, it is not necessarily around the mean opinion. Otherwise, the population polarizes around mildly extreme ones (around 0.2 and 0.8), avoiding the creation and maintenance of strongly extremist minorities, as it happens in the present model (see Supplementary Materials Figs. S8 – S11 ).

However, when interactions are mediated by a filtering algorithm - \(\gamma > 0\) , the media can attract a smaller fraction of the population since agents holding more extreme opinions are much less likely to interact with those in the sphere of influence of the moderate media. Overall, our experiments showed that the algorithmic bias maintains its fragmenting power: specifically, as the bias grows, the extremist clusters that coexist with the moderate one increase in size, but also in dispersion, eventually splitting into multiple smaller clusters. At the same time, the fragmenting effect of the recommender system decreases the size of the moderates/neutrals cluster, especially in the case of moderately close-minded populations (Fig. 2 ), but not in a significant way (at least with the population size considered in the present work). We include in the Supplementary Materials a comprehensive analysis of results of simulations with different parameter settings.

Extremist media shifts consensus in open-minded populations

Average number of clusters in the extremist setting. In the figure, the average number of clusters of the final opinion distribution is represented as a function of the algorithmic bias \(\gamma\) and the probability of user-media interaction \(p_m\) for different values of \(\epsilon\) . Values are averaged on 100 independent runs of each setting.

Average percentage of agents in the media cluster (0.0) in the extremist setting. In the figure, the average percentage of users in the extremist cluster ([0.0, 0.01]) is represented as a function of the algorithmic bias \(\gamma\) and the probability of user-media interaction \(p_m\) for different values of \(\epsilon\) . Values are averaged on 100 independent runs of each setting.

To investigate the effects of extremist propaganda and its effectiveness in shifting the consensus towards the desired opinion, we set the number of mass media outlets to \(M=1\) and the promoted opinion to \(x_m=0.0\) .

Like in the moderate setting, the baseline model’s cognitive and algorithmic biases effects also remain in this setting. In the same way, an increase in the frequency of interaction with extremist propaganda (when \(\gamma =0\) ) translates into an increase in the fragmentation of the final population. The number of clusters of the final opinion distributions, in fact, grows with \(p_m\) (Fig. 3 ). For example, when the population is close-minded ( \(\epsilon =0.2\) ), in the absence of propaganda ( \(p_m=0\) ), in the final state, there are two main clusters (on average), while as \(p_m\) increases, the number of clusters approaches 3. In the same way, as the population is more “open-minded” - so the number of clusters in the baseline model is lower - interacting with the propaganda still generates an increase in the number of clusters (moving the population from consensus around one opinion to clustering around two opinion values for \(\epsilon =0.3\) and also \(\epsilon =0.4\) , even if in this case on average there is a consistent majority cluster). Despite the fact that an extreme opinion is promoted (while, without external effects, agents tend to conform to moderate positions), in this case, bounded confidence or, in other words, the level of “open-mindedness” of the population, can be so high that all agents are eventually drawn towards the same opinion, as in the case of \(\epsilon =0.5\) (Fig. 3 d). This fact still holds when the interactions are mediated by a recommender system ( \(\gamma > 0\) ), biasing the choice of the interacting partner towards like-minded individuals, but it is less evident due to the fragmenting power of the algorithmic bias. For example, when the population is close-minded, we tend to have an average of three or four clusters in a biased environment.

It is important to note that, compared to the moderate situation, the fragmenting effect of the external media is stronger for an extremist message. The number of clusters reported in Fig. 1 is generally smaller than that reported in Fig. 3 .

In the present model, differently from the baseline 30 , i.e. \(p_m=0.0\) , the population splits into more than one cluster when \(\gamma > 0\) and \(\epsilon\) is sufficiently low. One of these clusters always forms around the extreme media opinion ( \(x_m=0.0\) ) while - as the bias grows - the rest of the population either clusters around a single value on the opposite side of the opinion spectrum or fragments into multiple small clusters (and their distance from the extremist propaganda increases with the open-mindedness of the population). This effect is stronger as the algorithmic bias increases and as the frequency of interaction with the media grows. In the case of extremist propaganda, as we can expect, a higher portion of the population in the stable state is an extremist, holding the same opinion promoted by the media (see Fig. 4 ). Additionally, the higher the open-mindedness of the population, i.e. the higher the confidence bound \(\epsilon\) , the higher the dimension of the extremist cluster - until ( \(\epsilon \ge 0.5\) ) the population is entirely attracted towards this extreme position (Fig. 4 d). However, as the bias increases, the final number of opinion clusters increases, and the average number of agents in the extremist cluster decreases: the fact that algorithmic bias increases fragmentation in the population causes - in this case - the formation and maintenance of an “opposition” cluster (see also Figs. S18 – S21 in the Supplementary Materials), countering the process of complete radicalization of the population. As the bias increases, of course, this cluster becomes more dispersed with respect to its average opinion, and for extreme biases, it fragments into a series of small opinion clusters. Therefore we can conclude that algorithmic bias acts as a partial protector against the message of one extremist media.

It is also worth noticing that, with \(p_m > 0\) , all other parameters being equal, the size of the extremist cluster does not increase with the probability of interaction with the media; on the contrary, the maximum size is reached for low or intermediate values of \(p_m\) (see Fig. 4 ). Also, in this case, such behavior is tied to the fact that, even if the frequency of interaction with the media increases, those agents that initially are within the sphere of influence of the media will converge towards an extremist position more rapidly, thus losing the ability to attract those who are outside of it. When dealing with close-minded agents, less frequent propaganda can attract a higher fraction of the population with respect to more intense propaganda. If the population is open-minded, the frequency of interactions with the media loses most of its discriminant power: if at least half of the agents are already initially influenceable by the media, the whole population will converge towards the media opinion.

Polarised media increase the divide

Average number of clusters in the polarised setting. In the figure, the average number of clusters of the final opinion distribution is represented as a function of the algorithmic bias \(\gamma\) and the probability of user-media interaction \(p_m\) for different values of the cognitive bias \(\epsilon\) . Values are averaged on 100 independent runs of each setting.

Public debates are often characterized by bi-polarity, a situation where two opposing views are proposed and debated. For example, media polarization in the U.S. has increased in the past half-decade, and both liberal and conservative partisan media are likely contributing to polarization in the Cable news networks 49 . While acknowledging that our synthetic setting represents a simplification of the complex dynamics at play, it nevertheless presents a scenario that merits further investigation. To recreate such a scenario - even if simplistically -, we simulated the presence of two extremist media outlets in the population, promoting opinions at the opposite sides of the opinion spectrum, - i.e. we set \(x_{m1} = 0.05\) and \(x_{m2}=0.95\) . As expected, the presence of two polarised media increases the system’s polarization, which would already naturally arise due to the effects of the cognitive and algorithmic biases ( \(\epsilon \le 0.3\) ), but the presence of the media pushes the population towards the media opinions - which are more extreme than the ones that naturally form in the baseline model (see Fig. 5 a,b and Fig. S24 in the Supplementary Materials). The presence of these two media, moreover, can bring the population towards polarization/fragmentation even in cases where the baseline model would predict full consensus ( \(\epsilon =0.4\) ), a fragmentation exacerbated by the recommender system effects (see Fig. 5 c,d). On the other hand, in “close-minded” populations, the byproduct of agent-to-media interactions increasing the number of opinion clusters is that the rapid polarization of the extremes of the population results in the formation of a cluster of “moderate” agents, coexisting with polarized groups. On the one hand, this reduces the level of polarization in the population with respect to the baseline model. On the other hand, the polarized subpopulations are more extremist than in the baseline. As the filtering power of the recommender system increases, such a moderate cluster splits into multiple small ones, still concentrated around the center of the opinion spectrum (see Figs. S29 – S32 in the Supplementary Materials for an example of the opinion evolution). Moreover, as the algorithmic bias grows, the two extremist clusters reduce their sizes, and more agents become neutral, even if they hold a wider range of opinions. This is because a reduced fraction of agents interacts with extremist media and/or peers that end up in the extremist cluster early in the process. Therefore, they cannot attract a more significant portion of the population with respect to the case where the filtering power of the recommender system is more robust. As the open-mindedness of the population grows, an increasingly stronger algorithmic bias is needed to maintain the moderate cluster, and, in most cases, the population tends to polarise, with the two sub-populations approaching the media opinions. The population is, in this scenario, ultimately radicalized around very extreme positions (0.05 or 0.95), similar to the case of a single extreme media. Finally, the recommender system makes the polarization process faster than what was observed in the baseline model, allowing fewer opinion clusters to coexist during the opinion dynamics.

Open-minded populations are unstable in a balanced media landscape

Average number of clusters in the balanced setting. In the figure, the average number of clusters of the final opinion distribution is represented as a function of the algorithmic bias \(\gamma\) and the probability of user-media interaction \(p_m\) for different \(\epsilon\) values. Values are averaged on 100 independent runs of each setting.

In the last setting, we considered a more balanced information environment, with the presence of two extremist media in the population, promoting opinions at the opposite sides of the opinion spectrum, - i.e. we set \(x_{m1} = 0.05\) and \(x_{m2}=0.95\) , alongside with a moderate media, with \(x_{m3}=0.5\) . In this setting, agents can retrieve from mass media both moderate and extremist points of view.

This more balanced news diet appears to still foster fragmentation. In fact, the higher the frequency of agent-to-media interactions, the more fragmented is the final population, as we can see from the average number of opinion clusters in the final population, which grows with \(p_m\) (Fig. 6 ) and from the average pairwise distance, indicating how far are the peaks in the final opinion distribution (see Fig. S35 in the Supplementary Materials).

In this case, the algorithmic bias maintains its fragmenting power for a close-minded population (i.e. \(\epsilon \le 0.3\) ). As the bias grows, the number of clusters increases, but it never exceeds three (Fig. 6 a,b) since the population tends to rapidly converge towards the media opinions (see Figs. S42 – S45 in the Supplementary Materials). The combination of a higher frequency of agent-to-media interactions, and the fact that interactions are biased towards similar opinions, allows each media to rapidly attract a portion of the population towards the promoted opinion.

On the other hand, in open-minded populations, \(\epsilon \ge 0.4\) , the relationship with the bias changes: from our experiments, it emerged that fragmentation is higher for low (Fig. 6 c) or intermediate (Fig. 6 d) values of the algorithmic bias \(\gamma\) , as the number of clusters in the final opinion distribution shows.

However, due to a stronger bias, the fragmentation that arises in the final state is not like the one reached in 30 . In that case, it was a stable state. In this case, the dynamic never reaches equilibrium, and agents keep changing their opinions influenced by the fixed opinions of the media. Nevertheless, in the cases where consensus can be reached, if open-mindedness is high, the dynamic is still unstable, and it takes a long time for the population to reach a consensus. Let us recall that the distance between two adjacent media is 0.45, so when \(\epsilon =0.4\) agents holding an opinion between 0.10 and 0.45 or between 0.55 and 0.9 can be attracted by the moderate media and one extremist media that falls within their confidence bound and this generates an unstable stationary state preventing the system from reaching equilibrium. Obviously, the higher the open-mindedness, the higher the number of clusters (and the average entropy of the final distribution) since agents are distributed on a wider opinion spectrum, and real clusters do not form. This effect is counteracted by a high algorithmic bias, which practically impedes the interaction with the furthest media, even if in the range of the confidence bound.

Algorithmic bias depolarizes discussion on EURO2020 “taking the knee” controversy

Joint distribution of the opinion of users and average leaning of their neighborhood. We display the first snapshot \(G_0\) (initial matches)( a ); the second snapshot \(G_1\) (quarter-finals to final) ( b ); the final state of the simulation of the Algorithmic Bias Model with Mass Media and Heterogeneous Confidence Bounds with \(p_m=0.5\) , \(\gamma =1.5\) and \(x_m=0.87\) ( c ); and the final state of the simulation of the Algorithmic Bias Model with Mass Media and Heterogeneous Confidence Bounds with \(p_m=0.5\) , \(\gamma =1.5\) and \(x_m=0.28\) ( d ).

Despite trying to capture possible real dynamics with mathematical models of opinion formation, such synthetic settings may fail to capture peculiar characteristics of real networks, e.g. scale-free degree distributions and modular structures, but also polarized initial conditions, which may characterize discussions around controversial topics. Such diverse conditions may lead to different conclusions than the ones obtained in the mean-field case. For this reason, we exploited an empirical network collected from Twitter during EURO2020, where Italian users expressed their stances on the controversy around taking the knee in favor of the Black Lives Matter protests 50 . We simulated our model using this network as starting condition (both topology and initial opinion distribution) for different values of the model’s parameters. We include the results of simulations of the various settings in the Supplementary Materials, while here we discuss the most important ones. Our findings suggest that consensus may be reached in the final state when considering a homogeneous confidence threshold in scenarios with no media present or only a single media source. Even if such results are not averaged over multiple runs, these results may imply that scale-free degree distributions and modular topologies enhance consensus when the population has a homogeneous level of bounded confidence that is not lower than 0.2. However, an exception arises when there are no media sources, and a parameter value of \(\gamma\) =1.5 is applied. In this case, the final opinion distribution becomes fragmented, characterized by two main clusters centered around the average leaning of the “pro” faction and the average leaning of the “against” faction (see Supplementary Figs. 51 – 54 ). In this case, the bias may be too strong for users to converge toward a common opinion. When two polarized media sources are introduced (see Supplementary Fig. 55 ), opinions are concentrated around a moderate opinion in the final distributions. It exhibits a Gaussian shape, suggesting that the population tends to converge towards a common opinion in this case too. However, the presence of polarized media may keep users leaning toward more extreme positions. Adding a “moderate” media to this scenario, our observations reveal that the final opinion distribution remains symmetric and peaked around the center of the opinion spectrum. However, the distribution variance decreases compared to the previous scenario, i.e. people tend to homologate even more around a single opinion value, and variability is reduced. Furthermore, as the bias ( \(\gamma\) ) increases, the variance continues to decrease, and for \(\gamma =1.0\) , a single main opinion cluster emerges in the final state. Nevertheless, if the bias increases, e.g. \(\gamma =1.5\) , the final distribution splits into distinct opinion clusters centered around the media opinion. Moreover, since assumptions of homogeneous parameters are considered unrealistic, we exploited a methodology developed in 51 to estimate user-level open-mindedness ( \(\epsilon _i\) ) and simulated a heterogeneous extension of our model. We include the results of simulations of this second set of experiments in the Supplementary Materials (see Supplementary Figs. 58 – 63 ), while here we discuss the most important ones. As displayed in Fig. 7 a, users were embedded into echo chambers around pro and against stances on the discussion during the first two matches. However, when considering the period from the quarter-finals to the final (Fig. 7 b), the same users are mainly clustered around positions in favor of kneeling, and polarization appears to be reduced. Simulations of our model, which exploits the first network as initial conditions of the simulations and accounts for heterogeneous levels of the confidence threshold estimated from the data according to the procedure in 51 , appear to confirm some of the insights offered by the mean-field analysis on the complete network with homogeneous parameters. The main conclusion that also holds in a real setting is that the algorithmic bias favors opinion fragmentation but, in doing so, helps to reduce the level of polarization of the network (see Fig. 7 c and d) when there is an external source (or even a highly influential user) promoting one stance over the other. However, the setting that better captures the real opinion evolution can be seen in Fig. 7 d, where a stubborn agent is promoting a fixed opinion aligned with the stance in favor of players “taking the knee”. However, in Fig. 7 c, where the media is aligned with the opposite stance, the community that becomes less polarized is the other one, differently from the real situation.

A bounded confidence model of opinion dynamics with algorithmic bias and mass media agents was presented and studied in a mean-field setting. The model is an extension of the Algorithmic Bias model 30 to include one or more mass media outlets. In the present work, media are modeled as stubborn agents, each promoting a fixed opinion and connected to every agent of the population. We analyzed four different settings, each representing a specific media landscape: in the first, a single moderate media is present; in the second, the single media supports extremist propaganda; in the third, two polarised media promote extreme and opposite opinions; and in the latter, a third media, promoting a moderate opinion, is added to the polarised setting. Our experiments reveal that mass media have an essential role in pushing people towards conformity and promoting the desired point(s) of view, but not in a straightforward manner, as adherence to the media message depends highly on cognitive and algorithmic bias and on the strength of the media itself. As we saw in the “moderate setting” (Sect. " Results "), an open-minded population tends to conform to moderate opinions, and only a few individuals will not. The main result of the “moderate message” is concentrating the central consensus cluster around the desired value. As expected, the size of the non-conforming clusters increases with algorithmic bias and decreases with open-mindedness. However, the size of the extremist nonconforming clusters also appears to increase in the strength of the moderate message. This is counterintuitive and indicates that, in general, not only the message has to be moderate, but also the frequency with which the message is presented has to be reduced. Moderation is necessary for all aspects to maximize adherence to the message.

Analyzing the results of the “extremist propaganda”, we saw that the power to push individuals towards the media opinion is not dependent on such opinion. In this case, the open-minded population tends to become extremist because agents are pushed toward the media opinion and conform to that value. Again, we observe that the maximum adherence to the media message is always obtained for moderate frequencies of interaction with media.

In a polarised media landscape, with two poles promoting extreme and opposite opinions, the more “open-minded” is the population - or, in other words, the easier it is to change peoples’ minds - the more likely the population will end up in one or two (oppositely) polarised extremist clusters. Also, in such a scenario, even when there would be a consensus around a moderate opinion, a higher frequency of interaction with the two extremist media is enough to push the population towards polarised stances, with two clusters forming around the media opinions.

In a balanced media landscape, when populations are close-minded, the more agents interact with mass media, the more they attract a portion of the population towards the promoted opinion. The effects of cognitive biases, i.e. bounded confidence, generally maintain the same role they have in the baseline model: the more “open-minded” is the population, the easier agents conform around the promoted opinion(s). However, when agents have access to multiple information sources (besides their peers’ opinions), “open-mindedness” leads to a population of indecisive individuals and unstable dynamics that prevent the system from reaching equilibrium.

Real network structures, characterized by scale-free degree distributions, modular structures, and polarized initial conditions, clearly impact the results of the dynamics of the present model. When open-mindedness is homogeneous across the population, users tend to converge towards a single opinion value, which depends on the initial average opinion and the opinion promoted by a single media. When the media landscape is more heterogeneous, i.e. media supporting two opposite stances, the population still tends to conform to a moderate stance. However, the final distribution has a higher variability, with some users maintaining more extreme leanings. Such variability is reduced when the media landscape actively promotes more moderate stances. In the case study, cognitive biases do not play a role in the result of the dynamics, while the role of the algorithmic bias remains the same as in the baseline model. However, when inferring open-mindedness levels from empirical data and using the real distribution of the parameter to simulate the model, results show final polarization distribution closer to the real ones, and the depolarizing role of the algorithmic bias emerges. Specifically, the real final state is well approximated by the setting where there is a recommender system biasing interactions and a mass media promoting an opinion aligned with the “pro-taking-the-knee” faction.

We typically give a positive value to a highly open-minded population, i.e. a population where agents have a high confidence bound. However, a higher open-mindedness in the presence of mass media may mean that the whole population is attracted to an extremist position, as we saw in the case of extremist propaganda or two polarised media. Even if the media is not extremist - it still means that the population conforms towards a single point of view, converging faster and perfectly towards a single opinion value, making agents subject to external control by those who can manipulate the information delivered by the media. Similarly, we usually give a positive value to the final consensus setting. However, as we already said, consensus also means conformity, homologation to a standard, which may be imposed from the outside and manipulated through media control to achieve the goals of those in power and hardly the optimal situation for our societies and democracies.

The large amount of research that has focused on detecting the strength and the effects of recommender systems and algorithmic biases moves from the idea that the presence of such biases traps users into echo chambers and/or filter bubbles, preventing them from getting confronted with a balanced information diet and thus polarising/fragmenting the population into a series of opinion clusters that do not communicate. Even though this is still far from being proven, even if we assume that this effect is true, it is worth asking ourselves whether this always has a negative effect. For example, from our work, it emerged that the presence of a recommender system alongside a moderate message facilitates the emergence and maintenance of extremist minorities, which coexist with a group of moderates. However, both a lower confidence bound, \(\epsilon\) , and a higher algorithmic bias, \(\gamma\) , when acting in a context where there is extremist propaganda or two polarised extremist media, avoid the complete radicalization/extremization of the whole population and counter the complete polarization by favoring the presence of a moderate cluster in both cases. We also observed that the recommender system facilitates convergence in a balanced setting where the population is open-minded. Indeed, it prevents the dynamic from being completely unstable - i.e. avoiding agents continuously changing their opinion and never reaching a stable state due to the presence of conflicting sources.

It is important to acknowledge that the identified effect of a recommender system is one of the potential outcomes, as also demonstrated through experiments conducted on a real network where two echo chambers were present. However, comprehending the full range of effects resulting from the actions of a recommender system involves considering multiple factors. Notably, this paper did not delve into the discussion of how incorporating the backfire effect 52 (that can be seen as a kind of confirmation bias), in addition to bounded confidence, could potentially lead to increased polarization and contradict the original intentions of the approach, which aim to depolarize. Theoretical studies that assess the impact of recommender systems and design them with various objective functions to reduce polarization 53 , 54 often overlook the consequences arising from the interplay of different cognitive biases. Consequently, while we have numerous theoretical findings, their validity hinges on understanding how users interact with information and modify their opinions. Hence, insights into user behavior and opinion changes are vital. This, for example, motivated our investigation in 51 to uncover the levels of cognitive biases exhibited by users within this discourse.

The present work is a preliminary step toward analyzing the interplay of social and media influence in digital environments and presents several limitations. We focused on mean-field scenarios, which prevents us from considering possible network effects on the results of the opinion evolution process. While this is a sound starting point, the obtained insights must be tested against different network structures or real networks to employ the proposed model to analyze and understand reality fruitfully. Moreover, social connections change in real settings, influencing subsequent interactions and opinion exchanges. As we did in 55 , 56 for the Algorithmic Bias model, network effects should be taken into account: greater sparsity in the underlying network structures appears to promote polarization and fragmentation in the Algorithmic Bias model, and it is possible that a similar effect may be observed in the model presented in this study.

We also saw in 55 that mesoscale structure may promote different outcomes on the dynamic based on the different initial conditions. Here, we studied this model on a real network that exhibits two polarized communities. Experiments suggest that this may favor consensus even for lower confidence threshold levels. In order to verify this hypothesis, more convergence analysis needs to be performed on different modular networks and with different initial conditions. The present model could then be studied on adaptive network topologies to understand the interplay of the dynamics on/of the network. Moreover, in our work, bias has a role in the choice of the media only when in the presence of two or more sources. Even in the presence of a single externally promoted opinion, some agents who are too far away from that position may still have a small probability of interacting with it. To account for such a pattern, the probability of interacting with the media - which is now homogeneous across the whole population - could be made heterogeneous and dependent on the distance between the agent’s opinion and the promoted opinion and heterogeneous levels of agents engagement with mass media can be integrated within the model. Although all the different models demonstrate that an open-minded population can reach a consensus on all issues, it is an unrealistic assumption. Regardless of how open-minded they may be, each user will still have an inherent preference towards one side of the opinion spectrum. To address this, we propose extending the current model to incorporate a baseline opinion that consistently influences the user in that direction. Finally, as we saw in 51 , real populations may have heterogeneous (opinion-dependent) levels of “open-mindedness”, which could be taken into account to specify agents’ peculiarities better (as we did within the case study on the Twitter EURO2020 network), as well as heterogeneous activity levels as in 57 . Similarly to “open-mindedness” and activity levels, we plan to augment the current model with data-driven insights on media bias and user interactions with mass media and authoritative voices via online social networks. This will enable us to understand better the long-term impact of such interactions and how their influence differs from that of peers. One missing aspect in this context is undoubtedly a “dynamic” behavior from users, including the creation/destruction of links and the evolution of \(\epsilon\) and \(p_m\) with increasing/decreasing polarization. Additionally, there needs to be more evolution in the media’s behavior or a more realistic user-media relationship. The media should be aware of the cognitive biases of their users, and not all media outlets have the entire population as their audience. The more polarized the media are, the more likely they are followed by only a portion of the already aligned population, thereby promoting ideas aligned with that population segment. Another aspect not considered is that in a real setting, the “media” or stubborn agents may not be mainstream media with which everyone can interact but specific influential users within the network. This model would need to be adapted in such a scenario, considering that these stubborn agents are no longer connected to the entire population but only to certain nodes. Furthermore, the nodes they are connected to might depend on the opinions of those nodes and the opinions they promote. While our model has some drawbacks, as discussed above, it also has some advantages: it is simple, it can be tested on various topologies, it considers psychological, technological, and external factors, and it allows for flexibility in the number and opinions of the media.

Model and methods

To introduce in the study of opinion dynamics the idea of a recommender system generating an algorithmic bias, the classical Deffuant-Weisbuch model 24 was extended previously, implementing the Algorithmic Bias model (or AB model, hereafter) 58 . Our work is an extension of the AB model to include external information. In this section, we will first describe the AB model briefly before detailing our extension.

The algorithmic bias model

In the AB model, we have a population of N agents, where each agent i has a continuous opinion \(x_{i} \in [0,1]\) . At every discrete time step, the model randomly selects a user pair ( i ,  j ), and if their opinion distance is lower than a predefined threshold \(\epsilon\) , \(|x_{i} - x_{j}| \le \epsilon\) , then the two agents change their opinion according to the following rule:

The parameter \(\epsilon \in [0,1]\) represents the confidence bound of the population, which is assumed to be constant and equal for all agents. Individuals can only be influenced by those with similar opinions; a population with a low \(\epsilon\) is said to be closed-minded; a high \(\epsilon\) , on the other hand, describes an open-minded population since it allows agents to influence each other even if their initial opinions are far away. The parameter \(\mu \in (0, 0.5]\) is a convergence parameter, modeling the strength of the influence the two individuals have on each other or, in other words, how much they change their opinion after the interaction. Even if there is no reason to assume that \(\epsilon\) and \(\mu\) should be constant across the population or at least symmetrical in the binary encounters, these parameters are always considered equal for every agent.

The dynamics described above are those of the Deffuant-Weisbuch model, well known and studied by the opinion dynamics community. The numerical simulations of this model show that the qualitative dynamic is dependent on \(\epsilon\) : as \(\epsilon\) grows, the number of final opinion clusters decreases. As for \(\mu\) and N , these parameters influence only the time to convergence and the final opinion distribution width.

The AB model is different in how the interacting pair is randomly selected. It introduces another parameter to model the algorithmic bias: \(\gamma \ge 0\) . This parameter represents the filtering power of a generic recommendation algorithm: if it is close to 0, the agent has the same probability of interacting with all its peers. As \(\gamma\) grows, so does the probability of interacting with agents holding similar opinions, while the likelihood of interacting with those who have distant opinions decreases. Therefore, this extended model modifies the rule to choose the interacting pair ( i ,  j ) to simulate a filtering algorithm’s presence. An agent i is randomly picked from the population, while j is chosen from i ’s peers according to the following rule:

where \(d_{ij} = |x_{i}-x_{j}|\) is the opinion distance between agents i and j . For \(\gamma = 0\) the model is equivalent to the DW-model.

Example of agent-to agent and agent-to-media interaction with \(\gamma =0.5\) and \(\epsilon =0.3\) . In the example, an agent with opinion 0.7 has a different probability to choose one of the four neighbors, represented by the thickness of the arrows in the figure. After changing opinions, due to the peer-to-peer interaction, the target agent chooses to interact with one of the three media, with a probability \(p_m\) . The choice of which media to interact with is determined according to \(\gamma\) , in the same way as in the social interaction: the higher the bias \(\gamma\) , the higher the probability to interact with a media promoting a closer opinion to the current one of the agent. If the media falls within the agent’s confidence bound \(\epsilon\) , the agent averages his opinion with the one of the media; otherwise, nothing happens. The media opinion, instead, remains unchanged.

The algorithmic bias model with mass media agents

We now present our extension of the AB model, tailored to analyze the effects of mass media propaganda. We chose to model mass media as stubborn agents connected to everyone in the population, i.e. agents whose opinions remain fixed during the dynamic process and can interact with the whole population. This choice simplifies real-world media outlets that may instead change the promoted point of view, being influenced by public opinion or politics. However, we assume that our analysis is temporally constrained and that such changes are unlikely. A completely mixed population model that every individual can use any media - offline and online - as an information source. The fact that individuals often have a limited set of sources among which they choose is due mainly to cognitive and technological biases, which effects we are trying to capture with this model. Finally, we allow an arbitrary number of media sources M instantiated with custom opinion distribution \(X_M\) to explore different scenarios in the present model.

To regulate the interactions with media outlets, we added another parameter, namely \(p_m \in [0, 1]\) , which indicates the probability that during each iteration of the model simulation - in addition to interacting with a peer - each agent interacts with a media \(j \in M\) - always selected according to Eq. ( 2 ). So at each step, t , a peer-to-peer interaction takes place - as in the AB model - and with probability \(p_m\) , the selected agent interacts with a news source.

When two agents interact, their opinions change if and only if the distance between their opinions is less than the parameter \(\epsilon\) , i.e. \(|x_{i}-x_{j}| \le \epsilon\) , according to Eq. ( 1 ). However, when agent j is a mass media, only the opinion \(x_i\) changes. Figure 8 illustrates an example of an interaction (both agent-to-agent and agent-to-media) and its effects on the node’s opinion in the presented model.

To conduct our experiments, we implemented the AB model with mass media within the NDlib 59 Python library. This library has many opinion dynamics and epidemic models and a large user base. By adding our model to the library we increase the availability of our model to the scientific community.

Analyses and measures

We simulate our model on a fully connected population of 100 agents, where the initial opinions are uniformly distributed, and we averaged the results over 100 runs. Like in 58 , to avoid undefined operations in Eq. ( 2 ), when \(d_{ik} = 0\) we use a lower bound \(d_{\epsilon } = 10^{-4}\) . We imposed the simulations to stop when the population reaches an equilibrium, i.e. the cluster configuration will not change anymore, even if the agents keep exchanging opinions. We also set an overall maximum number of iterations at \(10^6\) to account for situations where an equilibrium may never be reached. To better understand the differences in the final state, we studied the model for various combinations of the model parameters. We are interested in whether the different numbers and positioning of mass media and the growing interaction probability influence the final configuration, enhancing or reducing fragmentation and radicalizing individuals towards more extreme opinions, all other parameters being equal.

We replicated the work of 58 by setting a null probability to interact with the media to define a reliable baseline for comparison.

In the simulations, we evaluated the model on every combination of the parameters over the following values:

\(p_{m}\) takes values in [0.0, 0.5], with steps of 0.1 - where for \(p_{m}=0\) the model becomes the AB model.

\(\epsilon\) takes value in [0.1, 0.5], with steps of 0.1.

\(\gamma\) takes value in [0.5, 1.5], with steps of 0.25, and 0.0 - where for \(\gamma = 0\) and \(p_m=0\) the model becomes the DW-model.

\(\mu = 0.5\) , so whenever two agents interact, if their opinions are close enough, they update to the average opinion of the pair.

We analyzed different scenarios to understand the effects of (i) one media, either extreme with a fixed opinion of \(x_{m1}=0.0\) or moderate with an opinion of \(x_{m1}=0.5\) , (ii) two extremist media with \(x_{m1} = 0.05, x_{m2}=0.95\) and (iii) two extremist media and a moderate one with opinions \(x_{m1}=0.05, x_{m2}=0.5, x_{m3}=0.95\) .

We used different measures to interpret the results, each equally necessary to understand the final state of the population. The first and most intuitive measure to understand fragmentation is the number of clusters present on average at the end of the dynamic. We used a naive clustering technique to partition the final opinion distribution into clusters: we sorted the final opinions in each run and set a threshold. Starting from one extreme, the corresponding nodes belong to two clusters every time two consecutive opinions exceed the threshold. Optimal results were obtained using a threshold of 0.01. Once we divided the population into opinion clusters we compute the cluster participation ratio, as in 58 :

where \(c_i\) is the dimension of the i th cluster, i.e. the fraction of the population we can find in that cluster. In general, for n clusters, the maximum value of the participation ratio is n and is achieved when all clusters have the same size. At the same time, the minimum can be close to one if one cluster includes most of the population and a tiny fraction is distributed among the other \(n \min 1\) .

To grasp the attractive power of the media in each setting, we also computed the number of nodes present in the clusters centered on the media opinion. Specifically, we consider the percentage of agents that hold opinions in the range \([x_{m} - \lambda , x_m +\lambda ]\) with \(x_m\) being the media opinion and \(\lambda = 0.01\) .

The dataset used in this study spans approximately one month, from June 10th to July 13th, during which the EURO2020 matches were played. To focus our analysis on relevant conversations, we applied hashtag-based filtering, targeting discussions related to Italy’s matches, the tournament itself, and the topic of taking the knee. This filtering process yielded a collection of 38,908 tweets authored by 16,235 unique users.

We adopted a hashtag-based approach to infer Twitter users’ opinions regarding taking the knee during EURO 2020. A manual annotation process was employed to classify 2304 hashtags from the dataset. Each hashtag was assigned a numerical value based on its alignment with the pro or against stance, with \(\pm 3\) indicating a clear position, \(\pm 1\) indicating a close association, and 0 assigned to neutral or irrelevant hashtags. We calculated the non-neutral hashtag values within each tweet by averaging its classification value ( \(C_t\) ). Similarly, for each user ( u ), we computed their overall classification value ( \(C_u\) ) by averaging the classification values of their tweets. To facilitate integration with our opinion dynamics model, the initial pro/against scores, ranging from \(-3\) to 3, were normalized to a range of [0, 1]. Additionally, we discretized the leanings into three categories: “Pro” (if \(C_u \le 0.4\) ), “Against” (if \(C_u \ge 0.6\) ), and “Neutral” otherwise, encompassing users with highly polarized viewpoints.

From the collected data, we constructed an undirected attributed temporal network, where nodes represent users and edges capture their interactions, including retweets, mentions, quotes, and replies. The resulting network comprises 15,378 nodes and 36,496 edges. To serve as initial and final states for validating our model, we divided the network into two snapshots: the first corresponding to the group stage and round-of-16, and the second representing the period from the quarterfinals to the final. This division was chosen based on specific reasons that will be further specified. As our model does not consider the temporal evolution of links, we retained only the nodes present in both snapshots. The temporal element was disregarded, resulting in two undirected snapshot networks: \(G_0\) , with nodes labeled according to their leaning in the first period, and \(G_1\) , with nodes labeled according to their leaning in the second period. This simplification aligns with our model’s assumption of a static network. The two snapshot graphs consist of 2925 users (approximately 20% of the total) and 9081 edges. Notably, the giant connected component comprises 2894 nodes and 9054 edges. For further details on the description and characteristics of the network, please refer to the Supplementary Materials.

Experiments on real data

The experiments were carried out with the following parameters:

The underlying network structure is G : each node u in the interaction network is an agent i and each leaning \(C_u\) in \(G_0\) is an opinion \(x_i\) with \(x_i \in [0, 1]\) .

We tested both homogeneous and heterogeneous bounded confidence levels. For homogeneous values we considered \(\epsilon \in \{0.2, 0.3, 0.4\}\) ; for heterogeneous values, each agent i is assigned with a level of bounded confidence \(\epsilon _i\) obtained applying the procedure in 51 (see Algorithm 1 in Supplementary Materials) to \(G_0, G_1\) .

The parameter \(p_m\) takes values of either 0.0 (absence of mass media, the model becomes the Algorithmic Bias Model with heterogeneous \(\epsilon\) ) or 0.5.

The parameter \(\gamma\) varies in the range of [0.0, 1.5] with increments of 0.5; for \(\gamma =0.0\) , we obtain the Deffuant-Weisbuch model with heterogeneous \(\epsilon\) .

The parameter \(\mu\) is set to 0.5, i.e. when two agents interact, they adopt their average opinion.

The maximum number of iterations is set at \(10^5\) .

Simulations terminate when the maximum opinion change remains below a threshold of 0.01 for at least 500 consecutive iterations.

We performed a comprehensive analysis to examine the influence of different scenarios the opinion evolution. Our investigation encompassed five distinct media landscapes:

One mass media with opinion \(x_m = avg(pro) = 0.28\)

One mass media with opinion \(x_m = avg(neutral) = 0.49\)

One mass media with opinion \(x_m = avg(against) = 0.87\)

Two mass media with opinions \(x_{m1} = avg(pro) = 0.28\) and \(x_{m2} = avg(against) = 0.87\)

Three mass media with opinions \(x_{m1} = avg(pro) = 0.28\) , \(x_{m2} = avg(against) = 0.87\) and \(x_{m3} = avg(neutral) = 0.49\)

Since in these experiments every agent i has a different level of bounded confidence \(\epsilon _i\) , to account for parameter heterogeneity, we applied the opinion change rule of the Algorithmic Bias Model with Mass Media in the following way:

if \(d_{ij} < \epsilon _i\) \(x_i(t+1) = (x_i(t) + x_j(t))/2\)

if \(d_{ij} < \epsilon _j\) \(x_j(t+1) = (x_i(t) + x_j(t))/2\)

if if \(d_{ij}> \epsilon _i \,\, \& \,\,d_{ij} > \epsilon _j\) nothing happens

i.e. a heterogeneous version of the baseline model.

Since we performed only one run per scenario, it is not feasible to compute the same metrics used in the mean-field analysis. Therefore, we choose to compare the simulation outcomes under various conditions on the real network with the actual opinion values in \(G_1\) . This allows for a direct assessment of the simulation results against the empirical opinion data at the specified time point. Specifically, we conducted one simulation for each scenario and compared the results with \(G_1\) by examining the final states. To assess polarization and the presence of echo chambers in both real data and simulation outcomes, we adopted the approach presented in 60 . Supplementary Figs. 58 – 63 display plots showing the joint distribution of users’ opinions relative to the average leaning of their neighborhood, as obtained in our experiments. These plots provide insights into the formation of echo chambers within an interaction network by analyzing the behavior of individual nodes in relation to their neighbors’ behavior. As in 60 , we measure polarization in our simulation results based on the correlation between a user’s leaning and the average leaning of their nearest neighbors (ego network).

Data availibility

The datasets generated during this study for simulations of the Algorithmic Bias Model with Mass Media in the mean-field case are within this article. The EURO2020 datasets analysed during the current study are available in the AlgBiasMediaModel repository, https://github.com/ValentinaPansanella/AlgBiasMediaModel.git .

Code availability

The AB model with mass media implementation used to conduct our experiments is the one provided by the NDlib 59 Python library: http://ndlib.rtfd.io . Simulations can be reproduced using the code in: https://github.com/ValentinaPansanella/AlgBiasMediaModel.git .

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Valentina Pansanella

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Pansanella, V., Sîrbu, A., Kertesz, J. et al. Mass media impact on opinion evolution in biased digital environments: a bounded confidence model. Sci Rep 13 , 14600 (2023). https://doi.org/10.1038/s41598-023-39725-y

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The mass media and judgments of risk: Distinguishing impact on personal and societal level judgments

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Recent research findings about whether mass media reports influence risk-related judgments have not been consistent. One reconciliation of the differing findings is the impersonal impact hypothesis, which suggests that media impact occurs with societal level judgments about general problem importance or frequency but not with judgments about personal risks. Three studies, with 465 undergraduates were conducted to test this hypothesis. Results support the impersonal impact hypothesis by suggesting that personal and societal level judgments are distinct and that media reports exert their primary influence on societal rather than personal judgments. Although media reports influenced judgments about societal risks but not about risks to one's self under the conditions examined in the present research, personal judgments may be affected under other conditions. Conditions under which media reports may have differential or similar effects on personal and societal-level judgments are considered in relation to the base rates of an event occurring, the strength of the media case that a problem exists, and the individual's identification with the problem. (55 ref) (PsycINFO Database Record (c) 2006 APA, all rights reserved).

• mass media reports, societal vs personal level judgment of risk, college students

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T1 - The mass media and judgments of risk

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AU - Tyler, Tom R.

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Y1 - 1984/10

N2 - Recent research findings about whether mass media reports influence risk-related judgments have not been consistent. One reconciliation of the differing findings is the impersonal impact hypothesis, which suggests that media impact occurs with societal level judgments about general problem importance or frequency but not with judgments about personal risks. Three studies, with 465 undergraduates were conducted to test this hypothesis. Results support the impersonal impact hypothesis by suggesting that personal and societal level judgments are distinct and that media reports exert their primary influence on societal rather than personal judgments. Although media reports influenced judgments about societal risks but not about risks to one's self under the conditions examined in the present research, personal judgments may be affected under other conditions. Conditions under which media reports may have differential or similar effects on personal and societal-level judgments are considered in relation to the base rates of an event occurring, the strength of the media case that a problem exists, and the individual's identification with the problem. (55 ref) (PsycINFO Database Record (c) 2006 APA, all rights reserved).

AB - Recent research findings about whether mass media reports influence risk-related judgments have not been consistent. One reconciliation of the differing findings is the impersonal impact hypothesis, which suggests that media impact occurs with societal level judgments about general problem importance or frequency but not with judgments about personal risks. Three studies, with 465 undergraduates were conducted to test this hypothesis. Results support the impersonal impact hypothesis by suggesting that personal and societal level judgments are distinct and that media reports exert their primary influence on societal rather than personal judgments. Although media reports influenced judgments about societal risks but not about risks to one's self under the conditions examined in the present research, personal judgments may be affected under other conditions. Conditions under which media reports may have differential or similar effects on personal and societal-level judgments are considered in relation to the base rates of an event occurring, the strength of the media case that a problem exists, and the individual's identification with the problem. (55 ref) (PsycINFO Database Record (c) 2006 APA, all rights reserved).

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U2 - 10.1037/0022-3514.47.4.693

DO - 10.1037/0022-3514.47.4.693

M3 - Article

AN - SCOPUS:0001621691

SN - 0022-3514

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In This Article Expand or collapse the "in this article" section Selective Exposure

Introduction, general overviews.

• Preliminary Works
• Related Concepts
• Selective Exposure and Other Constructs
• Research on Mediating Factors
• Selective Exposure in the Context of Different Media
• Political Dimensions
• Social Dimensions

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Selective Exposure by Hans-Bernd Brosius , Christina Peter LAST REVIEWED: 23 February 2011 LAST MODIFIED: 23 February 2011 DOI: 10.1093/obo/9780199756841-0023

The basic assumption in the study of selective exposure is that people expose themselves to external stimuli in a selective way. When referred to the area of mass communication, this means that people choose certain types of media content and avoid other types. Although this fact may sound rather trivial, it is important in understanding the effects of mass communication because it is our common understanding that people can only be influenced by media messages to which they actually expose themselves. Therefore, the selective exposure concept emphasizes the active role of the individual in the selection of media content. Research into this phenomenon is undertaken in the fields of both psychology and communication studies. Basically, there are two major trends in this research. Most studies focus on factors that lead to selective exposure or that mediate this process, whereas other studies deal with the consequences of selective exposure to information processing. The selection processes have also been examined in different contexts, such as in political or online communication.

Only a few textbooks and anthologies have focused exclusively on selective exposure, and most overviews have been published in academic journals. Many works like Frey and Wicklund 1978 , Frey 1986 , and D’Alessio and Allen 2007 define “selective exposure” as the result of cognitive dissonance, which leads people to seek information consonant with their beliefs and to avoid challenging information. However, in Freedman and Sears 1965 , the authors concluded that there is little empirical support for these assumptions. This controversy in the early research on the subject has led to a multitude of studies exploring the effects of selective exposure. In contrast, Katz 1968 gives an overview of studies on voting behavior. Bryant and Davis 2006 as well as the contributors to Zillmann and Bryant 1985 look at selective exposure from a different perspective and focus on entertainment choices. The contributors to Hartmann 2009 , on the other hand, give a more general insight in the research of media choice.

Bryant, Jennings, and John Davies. 2006. Selective exposure processes. In Psychology of entertainment . Edited by Jennings Bryant and Peter Vorderer, 19–33. Mahwah, NJ: Erlbaum.

This undergraduate text focuses on the role of emotions in the selective exposure process and provides a useful overview of the subject in the context of entertainment choices. As in Zillmann and Bryant 1985 , selective exposure is conceptualized in a similar way to mood management theory, with the premise that people choose information according to their emotional state.

D’Alessio, Dave, and Mike Allen. 2007. The selective exposure hypothesis and media choice processes. In Mass media effects research: Advances through meta-analysis . Edited by Raymond W. Preiss, 103–119. Mahwah, NJ: Erlbaum.

This text provides three different meta-analytic reviews of studies dealing with selective exposure processes based on dissonance theory. It is extremely successful in imparting an overview of the empirical research on the subject. The authors replicate the analysis of Freedman and Sears 1965 and reach different conclusions.

Frey, Dieter, and Robert A. Wicklund. 1978. A clarification of selective exposure: The impact of choice. Journal of Experimental Social Psychology 14:132–139.

DOI: 10.1016/0022-1031(78)90066-5

This study identifies inconsistent findings concerning the selective exposure paradigm, which according to the authors, are attributable to inadequate methodological design and confounding factors. In this study, the variable of choice was manipulated, showing that selective exposure to supporting information increases when subjects perform a task voluntarily.

Frey, Dieter. 1986. Recent research on selective exposure to information. In Advances in experimental social psychology . Vol. 19. Edited by Leonard Berkowitz, 41–80. San Diego, CA: Academic Press.

This text gives a broad overview of the early research into selective exposure. It is useful for both undergraduates and graduates who wish to understand the origins of the concept and several of the variables that influence the selective exposure processes.

Freedman, Jonathan L., and David O. Sears. 1965. Selective exposure. In Advances in experimental social psychology . Vol. 2. Edited by Leonard Berkowitz, 58–98. San Diego, CA: Academic Press.

This text for advanced undergraduates and graduates provides a critical review of the research on selective exposure. It is one of the most cited and controversial articles discussed in the literature on selective exposure. After reviewing 23 studies on the subject, the authors conclude that the findings are inconsistent and, overall, there is little support for the selective exposure hypothesis.

Hartmann, Tilo, ed. 2009. Media choice: A theoretical and empirical overview. New York and London: Routledge.

Dealing with the different aspects of media choice, this anthology presents a modern view of selective exposure and examines the concept in a larger framework of media usage. The issues addressed in this volume concern the mechanisms that lead to the selection of media options and their consequences. It should be useful for both undergraduate and graduate students.

Katz, Elihu. 1968. On reopening the question of selectivity in exposure to mass communication. In Theories of cognitive consistency: A sourcebook . Edited by Robert P. Abelson, et al., 788–796. Chicago: Rand McNally.

This text is a review of the early research into selective exposure, focusing on exposure to mass communication. Katz lists studies outside cognitive dissonance theory, such as those on voting behavior. In contrast to other authors in the anthology, he finds considerable support for selective exposure theory.

Zillmann, Dolf, and Jennings Bryant, eds. 1985. Selective exposure to communication . Hillsdale, NJ: Erlbaum.

This anthology, directed toward both undergraduate and graduate students, focuses on selective exposure processes in the realm of entertainment. The contributions to this volume are from both psychology and communication departments and deal mainly with exposure to television programs.

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Sociologists study culture and the media in a variety of ways, asking a variety of questions about the relationship of culture to other social institutions and the role of culture in modern life. One important question for sociologists studying the mass media is whether these images have any effect on those who see them. The reflection hypothesis contends that the mass media reflect the values of the general population.

The media try to appeal to the most broad-based audience, so they aim for the middle ground in depicting images and ideas. Maximizing popular appeal is central to television program development; media organizations spend huge amounts on market research to uncover what people think and believe and what they will like. Characters are then created with whom people will identify. The images in the media with which we identify are distorted versions of reality. Real people seldom live like the characters on television, although part of the appeal of these shows is how they build upon, but then mystify, the actual experiences of people.

The reflection hypothesis assumes that images and values portrayed in the media reflect the values existing in the public, but the reverse can also be true— that is, the ideals portrayed in the media also influence the values of those who see them. As an example, social scientists have studied the stereotyped images commonly found in children's programming. Among their findings, they have shown that the children who watch the most TV hold the most stereotypic gender attitudes. Although there is not a simple and direct relationship between the content of mass media images and what people think of themselves, clearly these mass-produced images can have a significant impact on who we are and what we think.

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• v.107(3); Mar 2017

Mass Shootings: The Role of the Media in Promoting Generalized Imitation

J. N. Meindl took the lead in conceptualizing the topic and writing the text. J. W. Ivy contributed to developing the media suggestions and analysis. Both of the authors contributed to improving successive iterations of the text.

Mass shootings are a particular problem in the United States, with one mass shooting occurring approximately every 12.5 days.

Recently a “contagion” effect has been suggested wherein the occurrence of one mass shooting increases the likelihood of another mass shooting occurring in the near future. Although contagion is a convenient metaphor used to describe the temporal spread of a behavior, it does not explain how the behavior spreads. Generalized imitation is proposed as a better model to explain how one person’s behavior can influence another person to engage in similar behavior.

Here we provide an overview of generalized imitation and discuss how the way in which the media report a mass shooting can increase the likelihood of another shooting event. Also, we propose media reporting guidelines to minimize imitation and further decrease the likelihood of a mass shooting.

Mass shootings occur worldwide but are a particular problem in the United States. Despite being home to only 5% of the world’s population, roughly 31% of the world’s mass shootings have occurred in the United States. 1 As of 2015, a mass shooting resulting in the death of four or more people occurred approximately every 12.5 days. In addition to public massacres such as the shooting in an Orlando, Florida, nightclub in 2016, these figures include mass shootings related to gang activity or family slayings. Although there are many variables responsible for a mass shooting, and each instance is immediately precipitated by different events, the commonality is that a significant number of individuals are killed during the event.

Recently a contagion effect, similar to a “copycat” effect, has been suggested in mass shootings. This effect suggests that behaviors can be “contagious” and spread across a population. In the example of mass shootings, a contagion effect would be said to exist if a single mass shooting incident increased the likelihood of other instances of mass shootings in the near future. Contagion has been documented across a variety of other behaviors, including airplane hijackings, 2 smoking cessation, 3 and binge eating, 4 and has been well researched in relation to suicide. 5,6 There is now evidence that when a mass shooting occurs, there is a temporary increase in the probability of another event within the next 13 days on average. 7

Although understanding contagion allows for some degree of prediction that when one event occurs, a similar event is more likely to occur in the near future, it affords only prediction regarding temporal contiguity. The theory does not, for example, provide information on what factors might influence another person to commit a mass shooting or how the occurrence of a mass shooting can set the occasion for someone to commit a similar act.

CONTAGION VS GENERALIZED IMITATION

When applied to behavior, “contagion” is a metaphor borrowed from epidemiology to explain how behaviors can spread across a group of people. 8 Behaviors, however, are not diseases that can spread on contact. Essentially, contagion models an outcome—when someone engages in a behavior, there is a probability that someone else may do the same—but it does not describe the behavioral mechanism for the spread of the behavior. A better model is generalized imitation, which is well studied in the psychological literature 9 and can help explain the increased likelihood of people engaging in behaviors similar to those they have been made aware of or actually observed.

The difference between imitation and contagion is not merely one of semantics. Generalized imitation is the learned ability to perform behaviors that are similar to behaviors observed or described, even when performance is delayed. It is a skill that is acquired at an early age and gradually strengthened through many life experiences. Generalized imitation does not suggest that a person will always perform an exact copy of the model’s behavior; rather, it suggests that the person will perform a behavior with similar characteristics. For example, people imitating a boxer may not throw the same punches in the same sequence, but they will engage in similar boxing-like behaviors at a later point in the near future. If the likelihood of engaging in boxing-like behaviors were increased by observing someone else boxing, generalized imitation would be an important contributing factor.

Several variables affect generalized imitation. In general, people are more likely to imitate a model who is similar to themselves, particularly in terms of age and gender; who is of an elevated social status; who is seen being rewarded; and who is seen as competent. 10

THE ROLE OF MEDIA IN IMITATION

When mass shooters imitate other mass shooters, they are generally not imitating personally observed events (although this is possible in gang-related instances). In each case in which the event is unobserved, all information that could serve as a model for imitative behavior was provided via various media sources (legacy media, social media, new media), and research has demonstrated that media can influence imitation. 11 Not only do people often imitate behaviors that are portrayed in the media, the “reality” of the portrayal does not seem to have a significant influence. Imitation can occur regardless of whether the model is presented live, whether it is presented via film, 11 or even when the model’s behavior is merely described. 12

Importantly, the way that the media report an event can play a role in increasing the probability of imitation. When a mass shooting event occurs, there is generally extensive media coverage. This coverage often repeatedly presents the shooter’s image, manifesto, and life story and the details of the event, 13 and doing so can directly influence imitation.

Social status is conferred when the mass shooter obtains a significant level of notoriety from news reports. Images displaying shooters aiming guns at the camera project an air of danger and toughness. 14 Similarities between the shooter and others are brought to the surface through detailed accounts of the life of the shooter, with which others may identify. Fulfilled manifestos and repeated reports of body counts heap rewards on the violent act and display competence. Detailed play-by-play accounts of the event provide feedback on the performance of the shooter. All of these instances serve to create a model with sufficient detail to promote imitated mass shootings for some individuals.

DECREASING MASS SHOOTINGS: MEDIA AND IMITATION

If the manner with which the media (legacy, new, social) report a mass shooting event plays a role in promoting further mass shootings, changing these reporting methods could decrease imitation. This tactic has been effective in decreasing imitated suicide, 15 and the World Health Organization, citing 50 years of research on imitation, has posted media guidelines on reporting suicides to prevent imitational suicides. 16 The guidelines include suggestions such as not sensationalizing suicide (e.g., suggesting an “epidemic”), avoiding prominent headlines, not suggesting that suicide is caused by any single factor such as depression, not repeating the story too frequently, not providing step-by-step descriptions of methods, limiting use of photographs and videos, and being particularly careful with celebrity suicides.

Similar suggestions have been provided for reporting mass shootings. For instance, the Advanced Law Enforcement Rapid Response Training team, in collaboration with the Federal Bureau of Investigation, has developed the “Don’t Name Them” campaign. The campaign aims to curb media-induced imitational mass shootings and suggests minimizing naming and describing the individuals involved in mass shootings, limiting sensationalism, and refusing to broadcast shooter statements or videos. James Comey, director of the Federal Bureau of Investigation, followed a similar strategy in describing the 2016 shooting in Orlando:

You will notice that I am not using the killer’s name and I will try not to do that. Part of what motivates sick people to do this kind of thing is some twisted notion of fame or glory, and I don’t want to be part of that for the sake of the victims and their families, and so that other twisted minds don’t think that this is a path to fame and recognition. 17

Adopting the recommendations of the World Health Organization and the Advanced Law Enforcement Rapid Response Training team could help decrease the number of mass shootings in the United States.

There are additional strategies, suggested by research on generalized imitation, that media outlets might adopt to further minimize imitational mass shootings. One strategy could be to present the shooter’s actions in a negative light. Discussions of the actions of the shooter (e.g., preparation, planning, shooting) could portray these actions as shameful or cowardly. Associating observed behavior with punishment has been shown to decrease the likelihood of imitation. 18 Portraying the shooter’s behavior as shameful could decrease any perceived rewarding of the behavior, as emotional responses such as shame are generally not associated with positive outcomes.

A second strategy could be to avoid in-depth descriptions of the shooter’s rationale for engaging in the behavior. In general, people are more likely to imitate the behaviors of other people who they view as similar to themselves. When the media repeatedly describes a purported motive for the shooting they may inadvertently be pointing out similarities between the shooter and others that may have otherwise gone unnoticed. For example, stating that a shooter took revenge after years of bullying may portray a mass shooting as one possible response option for individuals experiencing bullying and with similar backgrounds as the shooter. Understanding the motive for a mass shooting is undoubtedly important, but in-depth descriptions of rationales may serve not only to inform but also to increase the likelihood of imitation.

A third strategy could be to reduce the overall duration of news coverage after a mass shooting. In the case of suicide, a dose–response relationship has been suggested wherein increased media coverage of a suicide event results in an increase in imitational suicides. 19 The same might be true for imitational mass shootings. There is a clamor for news after a mass shooting, and media coverage may continue for weeks. To the extent that media attention is perceived as rewarding the actions of the shooter through notoriety, thereby also increasing the social status of the shooter, decreasing overall media coverage may minimize the likelihood of imitation following a mass shooting event.

A fourth strategy could be to limit the use of live press events immediately following a mass shooting. Although there is a heightened desire for information in the immediate aftermath of a mass shooting, this information does not necessarily need to be offered in a live event, which might increase the overall level of “excitement” surrounding the event. Instead, information could be released via written updates. Not only would this minimize perceived reward, it might actually serve to decrease overall interest in the event, which might further curb imitation.

Similarly, it is important that new outlets present only the facts of a mass shooting rather than attempting to produce entertaining or dramatic digital re-creations of the event. There should be a clear attempt on the part of the media to reduce the frenetic energy or emotion of a “breaking news story.” Instead, the bare facts of the event should be conveyed in a straightforward or even dull manner to minimize interest in the event. Sensationalism should be avoided.

Finally, media reports should avoid providing detailed accounts of the actions of a mass shooter before, during, or after the event. Describing the shooter’s actions in extensive detail, or through graphical presentations, may provide additional information regarding the behaviors that might further prompt imitation. Instead, only the details necessary to describe the event should be provided. The less the behavior is described, the less likely it is to be imitated.

CONCLUSIONS

A mass shooting is a complex and destructive act that occurs as a result of many factors. One factor that is relevant to the spread of mass shootings and other “contagious” behaviors is generalized imitation. In instances of mass shootings, the media appear largely responsible for providing the model to imitate. Although there are a variety of strategies that could function in tandem to alter the likelihood of a mass shooting, changing the way the media report mass shootings is one important step in preventing and reducing imitation of these acts. Furthermore, it is likely that media-prompted imitation extends beyond mass shootings. A media effect has been shown with suicide, is implied in mass shootings, and may play a role in other extreme events such as home-grown terrorism and racially motivated crimes.

The responsibility for these acts does not reside with the media, but the media are an important vector for the spread of such behaviors. Changing the way in which the media report a mass shooting could be difficult given that sensationalizing a tragic event brings in both viewers and revenue, which is a powerful incentive. In addition, the continual creation and expansion of social and new media platforms may make change more difficult because, in these instances, individuals rather than larger corporate entities develop and disseminate media. Given the numerous media outlets that exist and the various motivations behind the posting of content, it is unlikely that the reforms suggested here could be effectively mandated.

However, public pressure could be exerted on the various media outlets and individual contributors to change their reporting tactics. In the case of new and social media, this same pressure could influence the various platforms to provide guidelines regarding uploaded content related to a mass shooting. The first step toward building this public pressure is to make the general public aware of the link between the media and generalized imitation, as well as the role the media play in unknowingly perpetuating acts of violence.

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Hypothesis in Mass Communication & Media Research Method

Hypothesis:

A  hypothesis  is a tentative statement about the relationship between two or more variables. A hypothesis is a specific, testable prediction about what you expect to happen in your study.

A hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore a number of factors to determine which ones might contribute to the ultimate outcome. In many cases, researchers may find that the results of an experiment  do not support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies. There are many ways to come up with a hypothesis. In many cases, researchers might draw a hypothesis from a specific theory or build on previous research.

A hypothesis states what we are looking for. When facts are assembled, ordered and seen in a relationship they constitute a theory. The theory is not speculation but is built upon fact. Now the various facts in a theory may be logically analyzed and relationships other than those stated in the theory can be deduced. At this point there is no knowledge as to whether such deductions are correct. The formulation of the deduction however constitutes a hypothesis; if verified it becomes part of a future theoretical construction.

A hypothesis looks forward. It is a proposition which can be put to a test to determine its validity. It may seem contrary to or in accord with common sense. It may prove to be correct or incorrect. In any event however, it leads to an empirical test. Whatever the outcome, the hypothesis is a question put in such a way that an answer of some kind can be forthcoming. The function of the hypothesis is to state a specific relationship between phenomena in such a way that this relationship can be empirically tested. The basic method of this demonstration is to design the research so that logic will require the acceptance or rejection of the hypothesis on the basis of resulting data.

A hypothesis should be stated clearly using appropriate terminology; testable; a statement of relationships between variables and limited in scope (focused).

Different Types of Hypothesis:

There are different types of hypotheses

Simple hypothesis : This predicts the relationship between a single independent variable and a single dependent variable.

Complex hypothesis : This predicts the relationship between two or more independent variables and two or more dependent variables.

Directional hypothesis:  They may imply that the researcher is intellectually committed to a particular outcome. They specify the expected direction of the relationship between variables i.e. the researcher predicts not only the existence of a relationship but also its nature.

 Non-directional   hypotheses : Used when there is little or no theory, or when findings of previous studies are contradictory. They may imply impartiality. Do not stipulate the direction of the relationship.

Associative and causal hypotheses : Propose relationships between variables – when one variable changes, the other changes. Do not indicate cause and effect.

  Causal hypothesis: Propose a cause and effect interaction between two or more variables.

The independent variable is manipulated to cause effect on the dependent variable.

The dependent variable is measured to examine the effect created by] the independent variable.

Null hypotheses : These are used when the researcher believes there is no relationship between two variables or when there is inadequate theoretical or empirical information to state a research hypothesis. Null hypotheses can be:

•   Simple or complex
•   Associative or causal

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Israel debunks ‘Hamas libels‘ about mass grave spread by media for internet clicks, says Netanyahu spokesman

T he Office of the Prime Minister of Israel on Friday flatly rejected the disinformation campaign waged by the terrorist movement Hamas that the Jewish State was involved in any misconduct regarding a mass grave found at a battle site located on a hospital compound in Gaza.

"Hamas libels know no limits. It's sad to see how many news organizations are still so quick to copy and paste Hamas's lies for clickbait. The IDF will continue to target Hamas while avoiding civilian casualties with precision likely to have never been seen in the history of warfare," Israeli government spokesman Avi Hyman told Fox News Digital.

Middle East expert Tom Gross told Fox News Digital that "Hamas has a long track record of fabricating the truth that puts even Al-Qaeda or Isis to shame. And yet, the supposedly responsible media — in particular, CNN on this occasion — repeat Hamas lies almost unquestioningly."

Gross added, "Even the BBC was not caught out on this occasion, but it seems CNN was all too eager to give credence to the latest Hamas blood libel against the Jewish state. When historians come to examine why there has been such a sharp increase in antisemitism in America this year, they may well examine the role of some media in encouraging it."

UN, HUMAN RIGHTS, MEDIA GROUPS RELY ON HAMAS DEATH TOLL IN 'SYSTEMATIC DECEPTION': EXPERT

The Hamas-run Civil Defense agency in Gaza said on Tuesday that Palestinian health workers uncovered nearly 340 bodies of people allegedly killed and buried by Israeli forces at Nasser Hospital in Khan Younis.

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When asked about the Hamas officials who claimed that the presence of hundreds of bodies in mass graves at the hospital compound in Khan Younis, U.S. State Department spokesman Vedant Patel said on Wednesday, "The allegations are troubling, they are disturbing, we take them very seriously, and we’re continuing to press the Government of Israel for more information. It’s our understanding the IDF has spoken to some of this publicly; they have stated that in search of Israeli hostages, they have uncovered graves in the area where Palestinians had previously been buried."

Israeli army spokesman Major Nadav Shoshani wrote on X, formerly known as Twitter, that "Misinformation is circulating regarding a mass grave that was discovered at Nasser Hospital in Khan Yunis. The grave in question was dug — by Gazans — a few months ago." 

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He continued: "This fact is corroborated by social media documentation uploaded by Gazans at the time of the burial, as seen in the video below. Any attempt to blame Israel for burying civilians in mass graves is categorically false and a mere example of a disinformation campaign aimed at delegitimizing Israel."

Prominent American statisticians have argued that  Hamas has fabricated death toll counts in the Gaza Strip to garner support among Western countries to end Israel’s campaign to root out the jihadi terrorist network. Hamas fails to distinguish between its terrorist combatants and civilians in its wars against Israel, military and statistical experts have long argued.

U.S. National Security Advisor Jake Sullivan told reporters about the mass grave, "We want answers," adding,"We want to see this thoroughly and transparently investigated." Hamas invaded Israel on October 7 and slaughtered 1,200 people, including over 30 Americans.

Hospitals are protected institutions during war under international law. However, the medical centers in the Gaza Strip have frequently been turned into de facto military installations by Hamas to wage war against Israel. Hospitals lose their protected rights when they are converted into military sites. Hamas uses medical compounds to hold hostages. 

Hamas currently has over 100 hostages, including Americans, in its captivity. The Hamas-held hostages are believed to be in Rafah, the main stronghold of Hamas battalions. 

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Omri Ceren, the national security advisor for U.S. Senator Ted Cruz (R-Texas), wrote on X about some of the mainstream reporting about the mass grave: "This is just something Hamas made up and CNN is amplifying it. I haven't even seen a defense of it. It's just CNN amplifying a thing that Hamas made up and then moving on."

The Times of Israel reported, in response to the Hamas accusation that Israel is to blame for the mass grave, that the "evidence has suggested this is false, with the bodies having previously been buried at that same location by Palestinians amid the fighting between Israeli forces and terror operatives in the area."

The UN’s controversial  human rights chief, Volker Türk, said he was "horrified" by reports of mass graves at the Gaza hospitals — Shifa medical compound in Gaza City and Nasser Hospital.

In January, Israel’s mission in Geneva, Switzerland, took Türk’s office to task for failing to call for the release of hostages in Gaza on the 100th day since the start of the war.

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The Office of the High Commissioner for Human Rights (OHCHR) is located in Geneva.

Fox News Digital press queries to the IDF and the State Department were not immediately answered.

Original article source: Israel debunks ‘Hamas libels‘ about mass grave spread by media for internet clicks, says Netanyahu spokesman

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Astrophysics > High Energy Astrophysical Phenomena

Title: swift x-ray and uv observations of six gaia binaries supposedly containing a neutron star.

Abstract: Recent observations have led to the discovery of numerous optically selected binaries containing an undetected component with mass consistent with a compact object (neutron star or white dwarf). Using the the Neil Gehrels Swift Observatory we have carried out X-ray and UV observations of a small sample of these binaries. Four systems are wide (with orbital period P>300 d), and they were chosen because of their small distance (d<250 pc) and the mass of the collapsed component favoring a neutron star. Two other are compact systems (P<0.9 d), with convincing evidence of containing a neutron star. The source 2MASS J15274848$+$3536572 was detected in the X-ray band, with a flux of 5E-13 erg/cm2/s and a spectrum well fitted by a power law or a thermal plasma emission model. This source also showed an UV (2200 Angstrom) excess, which might indicate the presence of mass accretion. For the other targets we derived X-ray flux upper limits of the order of 1E-13}$ erg/cm2/s . These results are consistent with the hypothesis that the collapsed component in these six systems are neutron stars.

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Frederick Your Local News for Frederick County, Maryland

Threat Of Mass Shooting Lands Man At Mental Health Facility After Reddit Post: Frederick Police

A perceived threat that circulated on social media involving an event in Frederick County caused temporary chaos for police and landed a man inside a mental health treatment facility.

Frederick Police investigated the curious Reddit post.

On Friday, officials say that the Frederick Police Department was advised of a post circulating on Reddit involving a man who stopped a resident downtown and "made a vague threat of a shooting to occur at an event in May." 

In the Reddit post - which can be found here - a user said that he was walking out of the Church Street garage when a man walked by him and said "be safe." 

"I asked him why I needed to be safe, and he said, 'Don't come here on May 5 or you will die of mass murder,'" the post states. "I asked him what he meant and he said 'You can either be smart or die on the street on May 5.' 

"I quickly took this picture of him before getting the f--k out of there and calling the police."

"We have already identified the individual associated with the alleged threat, and we are taking all necessary measures to address this situation," the department posted on Facebook Saturday morning. "While we understand the concern that arises from such reports, we urge the community to remain calm."

Later, officials provided more details.

Following that alert, the department tracked down the post, which included a photo of the alleged man making threats, and he was recognized by detectives and members of agency's crisis response team, who made a positive ID and took him into custody.

The man is being treated, police said, and will not be identified publicly "at this time." 

Officials are now working with county, state, and federal authorities to determine if criminal charges are to be filed.

"We want to reassure the community that your safety is our top priority, and we are treating this threat with the utmost seriousness," they said. "Our officers are vigilant and proactive, and we are working diligently to ensure that our streets remain safe."

This is a developing story. Check Daily Voice for updates.

Want breaking news in the DMV as it happens, or want to contribute? Join the DMV All Incidents Facebook group .

Click here to follow Daily Voice Frederick and receive free news updates.

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Burkina Faso suspends BBC and Voice of America after they covered a report on mass killings

FILE - A soldier applauds the presidential inauguration of Junta leader Lt. Col. Paul Henri Sandaogo Damiba during his swearing-in ceremony broadcast on national television on Wednesday, Feb. 16, 2022 in Ouagadougou, Burkina Faso. Burkina Faso suspended the BBC and Voice of America radio stations for their coverage of a report by Human Rights Watch on the mass killing of civilians carried out by the country’s armed forces. According to the report published by Human Rights Watch on Thursday, April 25, 2024, the army killed some 223 civilians, including 56 children, in villages accused of cooperating with militants. (AP Photo/Sophie Garcia, File)

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DAKAR, Senegal (AP) — Burkina Faso suspended the BBC and Voice of America radio stations for their coverage of a report by Human Rights Watch on a mass killing of civilians carried out by the country’s armed forces.

Burkina Faso’s communication spokesperson, Tonssira Myrian Corine Sanou, said late Thursday that both radio stations would be suspended for two weeks, and warned other media networks to avoid reporting on the story.

According to the report published by Human Rights Watch on Thursday, the army killed 223 civilians, including 56 children, in villages accused of cooperating with militants. The report was widely covered by the international media, including the Associated Press .

Burkina Faso, a once-peaceful nation, has been ravaged by violence that has pitted jihadis linked to al-Qaida and the Islamic State group against state-backed forces. Both sides have targeted civilians caught in the middle, displacing more than 2 million people , of which over half are children. Most attacks go unpunished and unreported in a nation run by a repressive leadership that silences perceived dissidents.

Earlier in April, the AP verified accounts of a Nov. 5 army attack on another village that killed at least 70 people . The details were similar — the army blamed the villagers for cooperating with militants and massacred them, even babies.

“VOA stands by its reporting about Burkina Faso and intends to continue to fully and fairly cover activities in the country,” the network said in a news article reporting on its suspension.

The BBC didn’t respond to a request for comment.

On Friday, the United Nations called on Burkina Faso to reverse the suspension of the two international broadcasters.

“Restrictions on media freedom and civic space must stop immediately. Freedom of expression including the right of access to information is crucial in any society, and even more so in the context of the transition in Burkina Faso,” it said in a statement.

In the same statement, the U.N. said it had received additional reports that large numbers of civilians, including children, had been killed in several villages in the Yatenga and Soum provinces of northern Burkina Faso. The AP couldn’t immediately verify those reports.

More than 20,000 people have been killed in Burkina Faso since jihadi violence linked to al-Qaida and IS first hit the West African nation nine years ago, according to the Armed Conflict Location and Event Data Project, a U.S.-based nonprofit group.

Burkina Faso experienced two coups in 2022. Since seizing power in September 2022 , the junta led by Capt. Ibrahim Traoré has promised to beat back militants. But violence has only worsened, analysts say. Around half of Burkina Faso’s territory remains outside of government control.

Frustrated with a lack of progress over years of Western military assistance, the junta has severed military ties with former colonial ruler France and turned to Russia instead for security support.