My focus is on The Spiral of Silence and social media. Current communications studies investigate the tendency not to voice opinions if perceived as against the majority publicly; this tendency is called “The Spiral of Silence.” The Spiral of Silence theory was published by Noelle-Neumann, E. (1984) and gained favor with researchers, and was discussed in terms of political view. My curiosity is founded in the Pew Research Center’s 2014 poll surveying willingness to share perspectives about the “Edward Snowden’s 2013 revelations of widespread government surveillance of Americans’ phone and email records.” My goal is to survey current research into the Spiral of Silence on social media.

Abstract

Elisabeth Noelle-Neuman wrote a groundbreaking book in communications in 1984. The Spiral of Silence: Public Opinion, Our Social Skin was the cumulation of her research beginning in 1959. The primary thesis of her work came to be known as the “Spiral of Silence” phenomenon. Others continued Noelle-Neuman’s research in communications, social sciences, and psychology over the past decades, and much has changed over the years. This paper will analyze Noelle-Neuman’s work and the communication situations that encompass that age. The following section will survey literature from 2013to 2022 concerning social media channels that have erupted over the century and their impact on the Spiral of Silence theory. Finally, this paper will highlight the differences in communication, opinion climate, and the new definitions of mass communication. Keywords: Spiral of silence, social media, artificial intelligence, persuasion, communication, Noelle-Neuman.

Spiral of Silence and social media

The Spiral of Silence theory states that most people have an unconscious fear of social Isolation, and this fear of Isolation triggers social monitoring behavior. These signals are primarily unconscious but consist of visual and audible cues and gestures. To avoid social Isolation, people refrain from stating views on controversial issues depending on the social climate for that issue. The opposite side of the Spiral of silence theory occurs when an individual feels their opinion is in favor of the social environment for the issue. This state is said to amplify the dominant side of the topic to drown out the other view (Noelle-Neumann, 1984a).

This paper examines current literature to find correlations or discrepancies in the Spiral of silence theory within social media channels. This literature survey located a few that can apply in today’s media culture. Looking into background and beginning studies beyond Noelle-Neumann and subsequent traditional media research, Donsback, Salmon, and Tsfati (2013) provide a collection of essays in an organized synopsis. Denis McQuail (2013) uses a paradigm to describe the broad aspect of study in media effects, from micro to macro in social analysis to attitude components. These variants in the study of attitude make up the accumulations in the theory’s research. McQuail calls for more research and harnessing social media as a tool. Jörg Matthes and Andrew F. Hayes (2013)are featured with a review of the empirical holes within the highly studied spiral theory. Then, CarrollJ. Glynn and Michael E. Huge (2013) compiled decades of meta-analysis in a concise tabular comparison and compiled results. The topic of social media and its influence on the Spiral of silence is peppered throughout the book. The statement “The Internet and particularly social media have facilitated the establishment of new social environments which are entities outside one’s privacy settings, but may remain stable although usual mechanisms of personal friendship, face-to-face meetings and a shared physical space of living do not apply” (Rössler & Schulz, 2013) sums up the complex changes online environments have made to public opinion expression. The following essays dissect the internet’s influence and guide further research. (Rusciano, 2013, p. 185)(Rosenthal & Detenber, 2013, p. 196)

Noelle-Neumann’s publication of the Spiral of Silence theory published in 1984 is a base reference for this investigation of current trends in public opinion and expression. It is a compilation of her findings within the pollical arena. Her research into the expression of public opinion is the foundation of many subsequent research efforts. The Spiral of Silence theory pins social isolation as a significant factor for not speaking toward opposing views and is based on traditional media channels, encompassing societal trends from an inter-relationship standpoint and the impact of mass media influence.

Donsback, Salmon, and Tsfati (2013) produced The Spiral of Silence: New Perspectives on Communication and Public Opinion, a volume collection of retrospective and contemporary contributions toward the Spiral of silence theory. It identifies criticism for the ideology and methods of the thesis and connects the information to relevant fields of study. This global anthology highlights analysis of the theory’s empirical research. The authors cite in the abstract the volume is “a key resource for future research and scholarship in communication, public opinion, and political science.” I find it valuable for my investigations as an inclusive summary up to 1993.

Sherice Gearhart and Weiwu Zhang (2015) continue the analysis in the light of “Increasing diversity of media content, selectivity, social networking site (SNS) interactivity, and the potential for anonymity have posed various challenges to its theoretical assumptions.” Using Pew the (2012) Search, Social Networks, and Politics research data, they tracked positive social network behavior (likes, shares, and positive feedback) against negative feedback producing a demographic comparison of the tendency to speak out compared to remaining silent. Their results provide a matrix for identifying social network response measurement and prove the Spiral occurs in online environments.

“Was It Something I Said?” “No, It Was Something You Posted!” A Study of the Spiral of Silence Theory in Social Media Contexts, Gearhart and Zhang (2015) explore social networking activity by examining the data from the 2012 Pew Research Center: Social Networks and Politics survey investigating the interactions of opinion expressions concerning agreeable and disagreeable content and the psychological factors of liberation within social media channels. According to Pew Research, the “survey was conducted from January 20-February 19, 2012 among 2,253 adults age 18 and over, including 901 cell phone interviews. Interviews were conducted in English and Spanish. The margin of error for the full sample is plus or minus two percentage points.” This research has a large enough study base but is still ten years old.

Collective Dynamics of the Spiral of Silence: The Role of Ego-Network Size by Shon and Geidner (2016) reflects on the Spiral of silence as the significant focus of public opinion research but fails to examine the theory in a global context. The analysis uses agent-based modeling to compile network distribution affected by widespread social media use. This study assumed that all agents could accurately perceive the proportions of supportive and opposite opinions within their networks, which may not always be the case.

Sohn and Geidner (2016) dissect the ego within global collective online environments, lending light to network size within the previous Spiral of silence research with a simulation in NetLogo. Sohn and Geidner measured conformity pressure with the variance of the number of opinion impacts, producing opinion dynamics with variable distributions in the network range. The effect of global majority-minority difference was averaged across 50 simulations, providing a dissection within a global context. The difference changed as network size increased. In addition, the study measured a person’s accuracy in a perceived majority against mean network size with a calculation of the global opinion climate using the sigmoid function. They derived a >90% accuracy. In their study, the Spiral of silence did not occur due to false perceptions of the majority opinion. The authors cite, “the population is divided into two (almost) remote islands perceiving the global opinion climate in a very accurate or utterly inaccurate way. An opinion embraced by the majority in a setting can subsequently shift to a minority status in another or vice versa”. The results from the ABM show the Spiral occurring at a decreasing rate, the minority never disappears, and a large-scale spiraling process does not happen.

Further investigation led to Chen (2018), who selected a moderated mediation model to analyze fear of social Isolation and willingness to self-censor contending on the size of the network. This study is limited to Hong Kong, making it a smaller source pool than the work of Sohn and Geidner and introducing a possible culture variant. Another variant in the research is of interest, however. Privacy is used as a mitigator, and behavior is identified by decreasing expression and withdrawing concerning political disagreement and publicness. Two dimensions are employed, individual personality trait (fear of Isolation) and digital affordance (privacy). The results show that when publicness and disagreement are middle to high social media users express fewer opinions. The research points to changes in the freedom of the press and polarized Chinese culture as a possible cause. Chen calls for individual platform research within the Spiral of silence theory in finalizing.

Chen’s (2018) study, Spiral of silence on social media and the moderating role of disagreement and publicness in the network: Analyzing expressive and withdrawal behaviors, examines the Spiral of silence process on social media with a subject base in Hong Kong employing two-wave panel data. Chen examines withdrawal and expressive behaviors and the moderating effect of disagreement and publicness on social media that influence the Spiral of silence. Chen found social Isolation to indirectly impact discouraging disagreeing opinion expression and enhancing willingness to self-censor within a network. Politics and publicness in the network moderate expressiveness and withdrawal. The test of variables produced no relation to expressing a supporting opinion. Chen’s recent research directly addresses whether the Spiral of silence is an entity within the social media context. However, I’m curious if the subject base in Hong Kong influences the results.

Liang’s (2018) paper Broadcast Versus Viral Spreading: The Structure of Diffusion Cascades and Selective Sharing on Social Media examines the spread of information on social media using a large-scale diffusion dataset from Twitter. The findings highlight that message spread through multiple steps is most likely to carry cross-ideologic information. The distance mediates this positive relationship between sharers and originators of the message. This data was suppressed by the number of connections of the sharers. Viral diffusion increases the chances of cross-ideological sharing. These findings are essential in that the connections are not necessarily within a defined community but impact opinion sharing by the perceived importance of the message.      

It would be essential to glimpse at information spread selective sharing behavior for any correlation with the Spiral of silence. Liang (2018) employs selective exposure with a broadcast or viral posting diffusion model. Liang surveys previous research, labeling political ideology a salient social identity and as a motivation for sharing and immediately perceived climate can increase sharing behavior. The study proposes that “community structure is among the major reasons for the positive relationship between cascade depth and cross-ideological sharing” (p. 539). The study was limited to Twitter posts from Congress members and examined the cascade involving 297,566 users. Twitter data allows intensive tracing of diffusion paths. The research targeted political keywords and popular political hashtags, noting that the presence of a hashtag increases the likelihood someone will share. The study excluded any post with fewer than five retweets producing a random sample of 70,000. The messages with more steps involve cross-ideological sharing mediated by the distance between sharers, concluding that viral diffusion increases cross-sharing and political diversity on social media.

Are social bots a real threat? (Ross et al., 2019) uses an agent-based model of the Spiral of silence to analyze the impact of manipulative actors in social networks and examines the relationship of automated intelligence in sharing information. Bots are constructed to input targeted information into web services, whether a web form or automated messages on social media. These messages influence sharing behavior and manipulating opinions. This study uses empirical evidence of individual behavior in an agent-based model. The model explicitly represents a network, and the model shows a correlation at the collective level. The most central actor in the network trends the consensus. In a highly polarized setting, considering network density and position of interjection into the network, a bot can tip the opinion climate in two out of three cases shaping the norms of social media users. These findings indicate outside forces beyond mass media and social influence that manipulate trends in public opinion.

Artificial intelligence (AI) or social bots are another entity that would influence findings toward a true spiral of silence (Ross et al., 2019). Ross et al. look into social bots’ aspects as automated actors manipulating public opinion and infiltrators in politics on social media, ultimately affecting opinion climate. Unlike previous research, the study is uniquely structured over a time scale. Their research implicates astroturfing, or the ability to blanket the internet with prescribed information. Ross et al. follow previous research to uncover the scale of influence and launch. The authors use empirical evidence of individual behavior in a Netlogo agent-based model API with a variable of bot characteristics and the social network structure. The bots were also introduced as agents with a static opinion. Bots were added to the equation after human agents due to their lack of connections. The authors introduced two variables for view with a continuous value for willingness to share opinions. The network position of the bots also directs their influence, and the authors considered network density in the results. Time was considered for reaching a consensus within social media channels, from seconds to decades. The findings indicate the influence wears off within a few steps of the simulation with human data. Once the bots were introduced, the global opinion climate tipped in 2 out of 3 cases regardless of the method of introduction, proving influence is affected by AI.

Sohn (2022) provides the final correlation by dissecting the difference between mass and social media in the Spiral of silence. Building on previous research, the author investigates the prevalence of the Spiral of silence within the more extensive network of social media channels. Sohn introduced the idea that mass media is a closed system of information. They are owner/director controlled, with a lack of competition, and the diversity of media options has yet to be explored. The complexity of information channels and the number of agents involved introduce complexity and unpredictability that previous models did not investigate. Time-based studies are just beginning to evolve to these attributes. Sohn also considered multi-step communication flow, mediation, and influencers psychologically and socially. According to the research, network clusters are also of influence, but the prevailing idea is social media connects like minds remotely. An individual’s perception of climate remained a variable, and frequency also provides sway. As opposed to previous investigations, Shon’s study creates a framework from the bottom up to capture micro-reactions. People observe the climate of information by volume, and their response is indicative of the volume they perceive. Overall personality is also a factor. The model constructed considers mass media as we know it today and the stream of information coming from social networks, giving each a value in the equation.

The influential publication, Spiral of Silence in the Social Media Era: A Simulation Approach to the Interplay Between Social Networks and Mass Media by Sohn (2022) focuses on the issue of curiosity. Is the Spiral of silence still relevant in contemporary settings? Sohn’s study employed agent-based modeling, “providing a way to observe a large number of distributed actors’ interactions over time in a simulated environment. Sohn explores social networks and mass media conditions that interact to create or suppress a large-scale spiral of silence. The study presents that the Spiral of silence is perceived locally. Still, due to the variety of media channels and individuals’ hyperconnectivity of today, a global-scale spiral of silence is less likely to occur. The many back-references make this article pivotal to my research paper.

Sohn’s research contains a contingency with media dependency. A person that consumes media above 70% inflates the proportion of influence. Embeddedness also plays a part in feeding the dependency, and the network size directs the amount of influence. Cluster maps illustrate how the two work together to mediate the Spiral of silence effect. Sohn employed Global sensitivity analysis to check the sensitivity of the model. The model relationships were studied by network size and mass media influence. Mass media was represented and dissected with social media influence, finding that opinion is directed by the media exposure outlet within the conditions of range and network. Democratic societies promote varied thought, but the opinion gap remained relatively small. These findings are explained by media polarization to conform to popular opinion. The contour maps of facets of social reach, media coverage, and the majority-minority opinion gap produced the same opinion gap as the control. Still, the maps highlighted social reach and media coverage, indicating that the more voices speak together, the more influence. The final analysis reflects that mass media and social media interplay considerably. Opinion distribution is intertwined with all media sources as Shon introduced probability into the equation with congruent and noncongruent media exposure, highlighting the environment as a critical influence factor. Shon finds that extremity in opinion causes rapid dissipation of conformity, but diversity in media outlets plays in the equation. Media information is diverse, including individual personality, social connections, and influence. The opinion gap varies based on social network size and influence, and the author notes that diversity and fragmentation are not in the equation. The dynamic pattern that social media introduces is complex to analyze, but the argument that social ties regulate opinion holds firm.

Conclusion

This collection of articles provides a snapshot of the effects of social media on the Spiral of Silence. The literature is not comprehensive but investigates aspects of the changing communications environment with the influence of the digital age. My initial reaction to these findings is that there needs to be more research for a definitive answer to whether the Spiral of Silence still exists as Noelle-Neumann (1984) constructed the theory. Sohn’s (2022) research is the pivot point for this investigation. But collecting data for outside influences, such as social-bot and other AI tactics (Ross et al., 2019), social media communication techniques (Liang, 2018), and integrating the effect of autonomy that social media provides in opinion expression (Chen, 2018; Donsbach et al., 2013; McQuail, 2013; Rössler & Schulz, 2013; Sohn, 2022; Sohn & Geidner, 2016) make consistent divisions in the original Spiral of Silence theory.

References

Chen, H.-T. (2018). Spiral of silence on social media and the moderating role of disagreement and publicness in the network: Analyzing expressive and withdrawal behaviors. New Media & Society, 20(10), 3917–3936. https://doi.org/10.1177/1461444818763384

Donsbach, W., Salmon, C. T., & Tsfati, Y. (2013). The Spiral of Silence: New Perspectives on Communication and Public Opinion. Taylor & Francis Group. http://ebookcentral.proquest.com/lib/utk/detail.action?docID=1588624

Gearhart, S., & Zhang, W. (2015). “Was It Something I Said?” “No, It Was Something You Posted!” A Study of the Spiral of Silence Theory in Social Media Contexts. Cyberpsychology, Behavior, and Social Networking, 18(4), 208–213. https://doi.org/10.1089/cyber.2014.0443

Glynn, C. J., & Huge, M. E. (2013). Speaking in Spirals: An Updated Meta-Analysis of the Spiral of Silence. In The Spiral of Silence. Routledge.

Liang, H. (2018). Broadcast Versus Viral Spreading: The Structure of Diffusion Cascades and Selective Sharing on Social Media. Journal of Communication, 68(3), 525–546. https://doi.org/10.1093/joc/jqy006

Matthes, J., & Hayes, A. F. (2013). Methodological Conundrums in Spiral of Silence Research. In The Spiral of Silence. Routledge.

Noelle-Neumann, E. (1984). The Spiral of silence: Public opinion, our social skin. University of Chicago Press.

NW, 1615 L. St, Washington, S. 800, & Inquiries, D. 20036 U.-419-4300 | M.-857-8562 | F.-419-4372 | M. (2012). Search, Social Networks and Politics Archives. Pew Research Center: Internet, Science & Tech. https://www.pewresearch.org/internet/dataset/february-2012-search-social-networking-sites-and-politics/

Paradigm Shifts in the Study of Media Effects: Denis McQuail. (2013). In The Spiral of Silence. Routledge.

Rosenthal, S., & Detenber, B. H. (2013). Cultural Orientation and the Spiral of Silence. In The Spiral of Silence. Routledge.

Ross, B., Pilz, L., Cabrera, B., Brachten, F., Neubaum, G., & Stieglitz, S. (2019). Are social bots a real threat? An agent-based model of the Spiral of silence to analyze the impact of manipulative actors in social networks. European Journal of Information Systems, 28(4), 394–412. https://doi.org/10.1080/0960085X.2018.1560920

Rössler, P., & Schulz, A. (2013). Public Opinion Expression in Online Environments. In The Spiral of Silence. Routledge.

Rusciano, F. L. (2013). World Public Opinion. In The Spiral of Silence. Routledge.

Sohn, D. (2022). Spiral of Silence in the Social Media Era: A Simulation Approach to the Interplay Between Social Networks and Mass Media. Communication Research, 49(1), 139–166. https://doi.org/10.1177/0093650219856510

Sohn, D., & Geidner, N. (2016). Collective Dynamics of the Spiral of Silence: The Role of Ego-Network Size. International Journal of Public Opinion Research, 28(1), 25–45. https://doi.org/10.1093/ijpor/edv005