Silent Majority and Vocal Minority are Significantly Different

Mustafaraj, E., Finn, S., Whitlock, C., & Metaxas, P. T. (2011). Vocal Minority Versus Silent Majority: Discovering the Opionions of the Long Tail. 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing, 103–110. https://doi.org/10.1109/PASSAT/SocialCom.2011.188

  • “We present results of data analysis that compares two groups of different users: the vocal minority (users who tweet very often) and the silent majority (users who tweeted only once). We discover that the content generated by these two groups is significantly different“
  • Difference in ditigal age: “members of the Silent group have been using Twitter for a longer time than those of the Vocal group”
  • Difference in online behaviour: “The vocal minority users link more to outside content, use more hashtags, and retweet more, all activities intended to broaden the impact and reach of tweets”

Why Silent? Key Theories

Spiral of Silence

What is spiral of silence

  • “To the individual, not isolating himself is more important than his own judgment.” – “More frightened of isolation than of committing an error, they joined the masses even though they did not agree with them”
  • “Thus the tendency of the one to speak up and the other to be silent starts off a spiraling process which increasingly establishes one opinion as the prevailing one.”

Key assumptions

  • Threat of isolation
  • Fear of isolation: conformity
  • Quasi-statistical sense: “As a result of fear of isolation, individuals constantly monitor their environment to check on the distribution of opinions as well as the future trend of opinion” (Scheufle & Moy, 2000)
  • Willingness to speak out and tendency to remain silent: “Individuals tend to publicly express their opinions and attitudes when they perceive their view to be dominant or on the rise. In contrast, when people sense their view is in the minority or on the decline, they become cautious and silent” (Scheufle & Moy, 2000)

Quantitative evidence

  • Scheufele, D. A., & Eveland, W. P., Jr. (2001). Perceptions of ‘public opinion’ and ‘public’ opinion expression. International Journal of Public Opinion Research, 13(1), 25–44. https://doi.org/10.1093/ijpor/13.1.25
  • Petrič, G., & Pinter, A. (2002). From social perception to public expression of opinion: A structural equation modeling approach to the spiral of silence. International Journal of Public Opinion Research, 14(1), 37–53. https://doi.org/10.1093/ijpor/14.1.37

Association with social capital

Dalisay, F., Hmielowski, J. D., Kushin, M. J., & Yamamoto, M. (2012). Social capital and the spiral of silence. International Journal of Public Opinion Research, 24(3), 325–345. https://doi.org/10.1093/ijpor/eds023

  • individual-level indicators (civic engagement, trust, and neighborliness) of social capital are associated with willingness to express opinions
  • individual-level indicators of social capital are associated with the perception that others support one’s opinions
  • perceived support for one’s opinions mediates the proposed relationship between individual-level social capital and willingness to express opinions

Association with cultural predispositions and news attention

Ho, S. S., Chen, V. H.-H., & Sim, C. C. (2013). The spiral of silence: Examining how cultural predispositions, news attention, and opinion congruency relate to opinion expression. Asian Journal of Communication, 23(2), 113–134. https://doi.org/10.1080/01292986.2012.725178

  • The concept of face refers to one’s social worth and image in social interactions
  • fear of isolation and saving face were negatively associated with individuals’ willingness to express their opinion on the issue
  • whereas news attention and issue salience were positively associated
  • fear of isolation was negatively associated with individuals’ willingness to offer a rationale for their opinion

Association with types of public (situational theory)

Lee, H., Oshita, T., Oh, H. J., & Hove, T. (2014). When do people speak out? Integrating the spiral of silence and the situational theory of problem solving. Journal of Public Relations Research, 26(3), 185–199. https://doi.org/10.1080/1062726X.2013.864243

  • active publics are more likely than other types of publics to express their opinions

Lee, N. Y., & Kim, Y. (2014). The spiral of silence and journalists’ outspokenness on twitter. Asian Journal of Communication, 24(3), 262–278. https://doi.org/10.1080/01292986.2014.885536

  • journalists who perceived a greater discrepancy between their opinions and the opinions of Twitter users about controversial issues in South Korea were less willing to voice their opinions on Twitter
  • politically conservative journalists were more likely to perceive that their opinions were in the minority; therefore, they were less likely than politically liberal journalists to discuss their opinions on Twitter because use of that particular technology is generally regarded in Korea as favored by liberals

Association with opinion climate

Duncan, M., Pelled, A., Wise, D., Ghosh, S., Shan, Y., Zheng, M., & McLeod, D. (2020). Staying silent and speaking out in online comment sections: The influence of spiral of silence and corrective action in reaction to news. Computers in Human Behavior, 102, 192–205. https://doi.org/10.1016/j.chb.2019.08.026

  • those who hold strong opinions are more likely to comment when they perceive the opinion climate to be oppositional rather than supportive to their worldview

Spiral of silence and social media

Neubaum, G., & Krämer, N. C. (2018). What do we fear? Expected sanctions for expressing minority opinions in offline and online communication. Communication Research, 45(2), 139–164. https://doi.org/10.1177/0093650215623837

  • in contemporary social networking websites, wherein users commonly face a personally relevant audience, people are prone to hold back their opinion as they expect losing control over the reactions of their audience

Purcell, K. H., Lee Rainie, Weixu Lu, Maria Dwyer, Inyoung Shin and Kristen. (2014, August 26). Social media and the ‘spiral of silence.’ Pew Research Center. https://www.pewresearch.org/internet/2014/08/26/social-media-and-the-spiral-of-silence/

  • In both personal settings and online settings, people were more willing to share their views if they thought their audience agreed with them.
  • Previous ‘spiral of silence’ findings as to people’s willingness to speak up in various settings also apply to social media users. Those who use Facebook were more willing to share their views if they thought their followers agreed with them.

Author, N. (2014, August 27). The ‘spiral of silence’ on social media. Pew Research Center. https://www.pewresearch.org/internet/2014/08/27/the-spiral-of-silence-on-social-media/

  • This survey shows how the social and political climate in which people share opinions depends on several other things:
    • Their confidence in how much they know. Those who felt they knew a lot about the issues were more likely than others to say they would join conversations.
    • The intensity of their opinions. Those who said they had strong feelings about the Snowden-NSA matter were more willing
    • Their level of interest. Those who said they were very interested in the Snowden-NSA story were more likely

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

  • fear of social isolation has an indirect effect on discouraging disagreeing opinion expression but not supporting opinion expression
  • fear of social isolation also has an effect on encouraging withdrawl behaviors through enhancing willingness to self-censor

Self-Censorship

Hayes, A. F., Glynn, C. J., & Shanahan, J. (2005). Validating the willingness to self-censor scale: Individual differences in the effect of the climate of opinion on opinion expression. International Journal of Public Opinion Research, 17(4), 443–455. https://doi.org/10.1093/ijpor/edh072

  • Self-censorship: the withholding of one’s opinion around an audience perceived to disagree with that opinion
  • some people rely on information about the climate of opinion more so than do others when they decide whether or not to voice their opinion publicly

Hayes, A. F., Scheufele, D. A., & Huge, M. E. (2006). Nonparticipation as self-censorship: Publicly observable political activity in a polarized opinion climate. Political Behavior, 28(3), 259–283. https://doi.org/10.1007/s11109-006-9008-3

  • People who are relatively more influenced by the climate of opinion when choosing whether or not to voice an opinion, are also “relatively less likely to engage in public political activities.”

Other Mechanisms

Censorship

Zhu, Y., & Fu, K. (2021). Speaking up or staying silent? Examining the influences of censorship and behavioral contagion on opinion (non-) expression in China. New Media & Society, 23(12), 3634–3655. https://doi.org/10.1177/1461444820959016

  • people tend to stay silent when the censorship in the global environment is intensive, whereas they tend to “rebel” against censorship by voicing their opinions, when they experience censorship themselves or witness censorship occurring to their friends or reference persons
  • Outspoken crowd could shield individuals from the fear of punishment and outspoken friends could mitigate individuals’ anger against censorship

Network structure

Sohn, D., & Choi, Y.-S. (2023). Silence in social media: A multilevel analysis of the network structure effects on participation disparity in facebook. Social Science Computer Review, 41(5), 1767–1790. https://doi.org/10.1177/08944393221117917

  • being active or silent in the social media environment is largely conditional on the surrounding network structures
  • individuals who are well-connected directly or indirectly with others, and/or merely belong to well-connected local communities are likely to post more messages
  • Please note that this conclusion reflects correlation, not causation

How to Infer their Opinion?

Use the Network Structure

Label Propagation

Abeysinghe, B., & Sunderraman, R. (2023). Inferring stances of silent-participants in twitter chatter using label propagation. 2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 824–831. https://doi.org/10.1109/IPDPSW59300.2023.00138

  • use graph methods to propagate the found opinions of users to silent participants
    • we can re-introduce the stance inference problem in our dataset as identifying the label of the node based on the influence it receives from surrounding labels of a graph
    • step 1 seed label: “we use weak labels coming from the participating users as they have linguistic clues”; hashtags are used as contexts
    • step 2 label propagation
  • Data: Twitter - misinformation relating to important medical facts: masks and vaccinations
  • Performance: 0.94 F1 score vs 0.49 F1 score (baseline - clustering)

Wang, L., Niu, J., Liu, X., & Mao, K. (2019). The Silent Majority Speaks: Inferring Silent Users’ Opinions in Online Social Networks. The World Wide Web Conference, 3321–3327. https://doi.org/10.1145/3308558.3313423

  • we infer the opinions of silent users by leveraging the text content posted by active users and their relationships between silent users
  • Theoretical base: existing studies on social science theories have verified that users that are connected (become friends) are prone to exhibit similar opinions on a certain topic
  • Data:
    • content features: message posted by oneself and other users in the same community
    • network topology
  • Method:
    • Clustering: users are clustered into communities
      • intra-community structural features: degree, shortest path, average distance, Katz centrality
      • inter-community structural features
    • Matrix Factorization: we design a coupled sparse matrix factorization (CSMF) model to capture the complex relations among these features
  • Performance: our CSMF model achieves over 80% accuracy for the inference of silent users’ opinions

Sun, C., Li, J., Fung, Y. R., Chan, H. P., Abdelzaher, T., Zhai, C., & Ji, H. (2023). Decoding the silent majority: Inducing belief augmented social graph with large language model for response forecasting (No. arXiv:2310.13297). arXiv. https://doi.org/10.48550/arXiv.2310.13297

  • we propose a novel framework, named SOCIALSENSE, that leverages a large language model to induce a belief-centered graph on top of an existent social network
  • Method:
    • building a belief-centered network on top of the existing social network
    • propagating information across multiple levels

Collaborative Filtering (User Homophily)

Zhou, Z., & Elejalde, E. (2024). Unveiling the silent majority: Stance detection and characterization of passive users on social media using collaborative filtering and graph convolutional networks. EPJ Data Science, 13(1), Article 1. https://doi.org/10.1140/epjds/s13688-024-00469-y

  • this study proposes and evaluates a new approach for silent users’ stance prediction based on collaborative filtering and Graph Convolutional Networks, which exploits multiple relationships between users and topics
  • Data: we examine user attitudes leading to the Chilean constitutional referendums in 2020 and 2022 through extensive Twitter datasets
  • Performance: outperforms the baselines by over 9% at the edge- and the user level
  • Method: identifying silent users’ engagement patterns with different topics and similarities to other, more active users
    • collaborative filtering: predict users’ association with a topic and the various perspectives within each discussion based on hashtags
    • Weighted-LightGCN: the model jointly updates the representations of users and hashtags by aggregating the neighbors’ features

Use the Text on Other Topics

Gong, W., Lim, E.-P., Zhu, F., & Cher, P. H. (2016). On unravelling opinions of issue specific-silent users in social media. Proceedings of the International AAAI Conference on Web and Social Media, 10(1), Article 1. https://doi.org/10.1609/icwsm.v10i1.14722

  • Opinion Survey
    • we conduct an opinion survey on a set of users for two popular social media platforms, Twitter and Facebook
    • more than half of our users who are interested in issue i are i-silent users in Twitter
    • i-silent users are likely to have different opinion distribution from the users who post about i
  • Opinion Prediction
    • Data: sentiment features (from user’s content) and opinion features (from user’s predicted opinion or opinion on other issues)
    • Performance: predicting i-silent users’ opinions can achieve reasonably good accuracy from user posted content that is not related to issue i, and achieve better accuracy when we make use of user opinions on other issues

Anomaly Detection

Cui, H., & Abdelzaher, T. (2022). The voice of silence: Interpreting silence in truth discovery on social media. Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 82–89. https://doi.org/10.1145/3487351.3488360

  • This work focuses on predicting the absent links in the retweet graph
  • They formulate a joint fact-finding and silence interpretation problem, and shows that the joint formulation significantly improves our ability to distinguish true and false claims
  • An unsupervised algorithm, Joint Network Embedding and Maximum Likelihood (JNEML) framework, is developed to solve this problem
  • Input: dataset of posts - a content (assertion), a type (retweet-a measure of joint interest), a source (user)
    • content(post/retweet) relations
    • semantic similarity between assertions
  • Anomaly detection: the target is to identify normal or anomalous silence
    • The target is interpreted as a network embedding problem
    • To find what is “normal” for a user to post on, and what is “normal” for a user to retweet