The Influence of Collective Opinion on True-False Judgment and Information-Sharing Decision

How does the collective opinion of crowds influence people’s credibility judgment and sharing likelihood of health-related statements in social media? The work reported here addressed this question through two experiments. The results of Experiment 1 revealed that the crowd adopted the collective credibility judgment when evaluating the credibility of a statement. Similarly, the results of Experiment 2 showed that the crowd followed the collective sharing likelihood when rating the likelihood of sharing a statement. This social influence took place for statements that were perceived as true, debatable, and false, indicating that the effect of collective opinion was strong.

[1]  Krishna P. Gummadi,et al.  Measuring User Influence in Twitter: The Million Follower Fallacy , 2010, ICWSM.

[2]  Sung-Hwan Kim,et al.  Predicting the Virtual Temperature of Web-Blog Articles as a Measurement Tool for Online Popularity , 2011, 2011 IEEE 11th International Conference on Computer and Information Technology.

[3]  Bernardo A. Huberman,et al.  The Pulse of News in Social Media: Forecasting Popularity , 2012, ICWSM.

[4]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[5]  Howard L. Fromkin,et al.  Uniqueness, the human pursuit of difference , 1980 .

[6]  Duncan J. Watts,et al.  Financial incentives and the "performance of crowds" , 2009, HCOMP '09.

[7]  Kelly Tepper Tian,et al.  Consumers' Need for Uniqueness: Scale Development and Validation , 2001 .

[8]  Matthew J. Salganik,et al.  Leading the Herd Astray: An Experimental Study of Self-fulfilling Prophecies in an Artificial Cultural Market , 2008, Social psychology quarterly.

[9]  Muzafer Sherif,et al.  A study of some social factors in perception. , 1935 .

[10]  Jeffrey V. Nickerson,et al.  2377 People Like this Article: The Influence of Others’ Decisions on Yours , 2009 .

[11]  L. Festinger A Theory of Social Comparison Processes , 1954 .

[12]  Cameron Marlow,et al.  A 61-million-person experiment in social influence and political mobilization , 2012, Nature.

[13]  JoongHo Ahn,et al.  Why Are You Sharing Others' Tweets?: The Impact of Argument Quality and Source Credibility on Information Sharing Behavior , 2011, ICIS.

[14]  S. Asch Opinions and Social Pressure , 1955, Nature.

[15]  T. Shibutani Improvised News: A Sociological Study of Rumor , 1966 .

[16]  Yasuaki Sakamoto,et al.  Toward a Social-Technological System that Inactivates False Rumors through the Critical Thinking of Crowds , 2013, 2013 46th Hawaii International Conference on System Sciences.

[17]  Yasuaki Sakamoto,et al.  Following Trendsetters: Collective Decisions in Online Social Networks , 2012, 2012 45th Hawaii International Conference on System Sciences.

[18]  Michael D. Buhrmester,et al.  Amazon's Mechanical Turk , 2011, Perspectives on psychological science : a journal of the Association for Psychological Science.

[19]  C. Heath,et al.  Do People Prefer to Pass Along Good or Bad News? Valence and Relevance of News as Predictors of Transmission Propensity , 1996, Organizational behavior and human decision processes.

[20]  Paul P. Maglio,et al.  Categorization in the wild , 2008, Trends in Cognitive Sciences.

[21]  Fang Wu,et al.  Social Networks that Matter: Twitter Under the Microscope , 2008, First Monday.

[22]  P. Bordia,et al.  Rumor Psychology: Social and Organizational Approaches , 2006 .

[23]  Xin Shuai,et al.  Multiple spreaders affect the indirect influence on twitter , 2012, WWW.

[24]  Hosung Park,et al.  What is Twitter, a social network or a news media? , 2010, WWW '10.

[25]  Sinan Aral,et al.  Identifying Influential and Susceptible Members of Social Networks , 2012, Science.

[26]  Barbara Poblete,et al.  Information credibility on twitter , 2011, WWW.

[27]  R. H. Knapp,et al.  A PSYCHOLOGY OF RUMOR , 1944 .

[28]  Tad Hogg,et al.  Using a model of social dynamics to predict popularity of news , 2010, WWW '10.

[29]  Todd M. Gureckis,et al.  CUNY Academic , 2016 .

[30]  Panagiotis G. Ipeirotis,et al.  Running Experiments on Amazon Mechanical Turk , 2010, Judgment and Decision Making.

[31]  H. Kelman Compliance, identification, and internalization three processes of attitude change , 1958 .

[32]  Noah J. Goldstein,et al.  Social influence: compliance and conformity. , 2004, Annual review of psychology.

[33]  M. Deutsch,et al.  A study of normative and informational social influences upon individual judgement. , 1955, Journal of abnormal psychology.

[34]  Robert L. Goldstone,et al.  Thinking in groups , 2006 .

[35]  Yasuaki Sakamoto,et al.  The Impact of Collective Opinion on Online Judgment , 2010 .

[36]  Jon Kleinberg,et al.  Maximizing the spread of influence through a social network , 2003, KDD '03.

[37]  Frank Schweitzer,et al.  Emotional Divergence Influences Information Spreading in Twitter , 2012, ICWSM.

[38]  Rineke Verbrugge,et al.  Proceedings of the 37th Annual Meeting of the Cognitive Science Society, CogSci 2015, Pasadena, California, USA, July 22-25, 2015 , 2015, CogSci.

[39]  Matthew J. Salganik,et al.  Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market , 2006, Science.

[40]  C. Heath,et al.  Where Consumers Diverge from Others: Identity Signaling and Product Domains , 2007 .

[41]  Siddharth Suri,et al.  Conducting behavioral research on Amazon’s Mechanical Turk , 2010, Behavior research methods.

[42]  S. Anthony,et al.  Anxiety and rumor. , 1973, The Journal of social psychology.

[43]  Chip Heath,et al.  Evolving Informational Credentials: The (Mis)Attribution of Believable Facts to Credible Sources , 2004, Personality & social psychology bulletin.