Social Influence and Unfollowing Accelerate the Emergence of Echo Chambers

While social media make it easy to connect with and access information from anyone, they also facilitate basic influence and unfriending mechanisms that may lead to segregated and polarized clusters known as "echo chambers." Here we study the conditions in which such echo chambers emerge by introducing a simple model of information sharing in online social networks with the two ingredients of influence and unfriending. Users can change both their opinions and social connections based on the information to which they are exposed through sharing. The model dynamics show that even with minimal amounts of influence and unfriending, the social network rapidly devolves into segregated, homogeneous communities. These predictions are consistent with empirical data from Twitter. Although our findings suggest that echo chambers are somewhat inevitable given the mechanisms at play in online social media, they also provide insights into possible mitigation strategies.

[1]  Miriam J. Metzger,et al.  The science of fake news , 2018, Science.

[2]  Johan Bollen,et al.  Happiness Is Assortative in Online Social Networks , 2011, Artificial Life.

[3]  Damon Centola Damon Centola Behavior An Experimental Study of Homophily in the Adoption of Health , 2011 .

[4]  Jan Lorenz,et al.  The triple‐filter bubble: Using agent‐based modelling to test a meta‐theoretical framework for the emergence of filter bubbles and echo chambers , 2018, The British journal of social psychology.

[5]  S. Page Prologue to The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies , 2007 .

[6]  N. Stroud Polarization and Partisan Selective Exposure , 2010 .

[7]  Jesse M. Shapiro,et al.  Ideological Segregation Online and Offline , 2010 .

[8]  R. Nickerson Confirmation Bias: A Ubiquitous Phenomenon in Many Guises , 1998 .

[9]  Van AlstyneMarshall,et al.  Global Village or Cyber-Balkans? Modeling and Measuring the Integration of Electronic Communities , 2005 .

[10]  Michael Vitale,et al.  The Wisdom of Crowds , 2015, Cell.

[11]  Joshua B. Plotkin,et al.  Information gerrymandering and undemocratic decisions , 2019, Nature.

[12]  Jure Leskovec,et al.  {SNAP Datasets}: {Stanford} Large Network Dataset Collection , 2014 .

[13]  Ullrich K. H. Ecker,et al.  Beyond Misinformation: Understanding and coping with the post-truth era , 2017 .

[14]  Thomas C. Schelling,et al.  Dynamic models of segregation , 1971 .

[15]  Alain Barrat,et al.  Consensus formation on coevolving networks: groups' formation and structure , 2008, 0801.4860.

[16]  A. Barrat,et al.  Consensus formation on adaptive networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  R. Kelly Garrett,et al.  Echo chambers online?: Politically motivated selective exposure among Internet news users , 2009, J. Comput. Mediat. Commun..

[18]  Jiye Baek,et al.  Network structure and patterns of information diversity on Twitter , 2016, MIS Q..

[19]  Russell K. Nieli The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies, by Scott E. Page. Princeton, NJ, and Oxford: Princeton University Press, 2007, 448 pp., $27.95 hardbound, $19.95 paperback. , 2009 .

[20]  Harry Eugene Stanley,et al.  Modeling confirmation bias and polarization , 2016, Scientific Reports.

[21]  Jacob Ratkiewicz,et al.  Political Polarization on Twitter , 2011, ICWSM.

[22]  Andreas Flache,et al.  Communication in Online Social Networks Fosters Cultural Isolation , 2018, Complex..

[23]  G. Caldarelli,et al.  The spreading of misinformation online , 2016, Proceedings of the National Academy of Sciences.

[24]  Igor M. Sokolov,et al.  Modeling echo chambers and polarization dynamics in social networks , 2019, Physical review letters.

[25]  Lars Backstrom Serving a Billion Personalized News Feeds , 2016, WSDM '16.

[26]  Paul Windrum,et al.  Empirical Validation of Agent-Based Models: Alternatives and Prospects , 2007, J. Artif. Soc. Soc. Simul..

[27]  Damon Centola,et al.  The wisdom of partisan crowds , 2019, Proceedings of the National Academy of Sciences.

[28]  Sinan Aral,et al.  The spread of true and false news online , 2018, Science.

[29]  Yi Yu,et al.  Opinion diversity and community formation in adaptive networks , 2017, Chaos.

[30]  Filippo Menczer,et al.  Quantifying Biases in Online Information Exposure , 2018, J. Assoc. Inf. Sci. Technol..

[31]  Noah E. Friedkin,et al.  A Structural Theory of Social Influence: List of Tables and Figures , 1998 .

[32]  Jure Leskovec,et al.  Microscopic evolution of social networks , 2008, KDD.

[33]  A. L. Schmidt,et al.  Anatomy of news consumption on Facebook , 2017, Proceedings of the National Academy of Sciences.

[34]  David G. Rand,et al.  The Emergence of “Us and Them” in 80 Lines of Code , 2014, Psychological science.

[35]  Alvin Zhou #Republic: Divided Democracy in the Age of Social Media , 2017 .

[36]  Jon M. Kleinberg,et al.  The Web as a Graph: Measurements, Models, and Methods , 1999, COCOON.

[37]  James P. Gleeson,et al.  Competition-induced criticality in a model of meme popularity , 2013, Physical review letters.

[38]  Eli Pariser FILTER BUBBLE: Wie wir im Internet entmündigt werden , 2012 .

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

[40]  Filippo Menczer,et al.  The production of information in the attention economy , 2014, Scientific Reports.

[41]  Aristides Gionis,et al.  Political Discourse on Social Media: Echo Chambers, Gatekeepers, and the Price of Bipartisanship , 2018, WWW.

[42]  P. V. Marsden,et al.  Homogeneity in confiding relations , 1988 .

[43]  A Vespignani,et al.  Topical interests and the mitigation of search engine bias , 2006, Proceedings of the National Academy of Sciences.

[44]  Filippo Menczer,et al.  Partisan asymmetries in online political activity , 2012, EPJ Data Science.

[45]  Lorien Jasny,et al.  An empirical examination of echo chambers in US climate policy networks , 2015 .

[46]  Matthew J. Lindberg,et al.  Feeling validated versus being correct: a meta-analysis of selective exposure to information. , 2009, Psychological bulletin.

[47]  E. Bonabeau Decisions 2.0: the power of collective intelligence , 2009 .

[48]  Guillaume Deffuant,et al.  Mixing beliefs among interacting agents , 2000, Adv. Complex Syst..

[49]  Alireza Sahami Shirazi,et al.  Limited individual attention and online virality of low-quality information , 2017, Nature Human Behaviour.

[50]  M. Newman,et al.  Nonequilibrium phase transition in the coevolution of networks and opinions. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[51]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[52]  Lada A. Adamic,et al.  Friends and neighbors on the Web , 2003, Soc. Networks.

[53]  A. Vespignani,et al.  Competition among memes in a world with limited attention , 2012, Scientific Reports.

[54]  Eli Pariser,et al.  The Filter Bubble: What the Internet Is Hiding from You , 2011 .

[55]  Alessandro Vespignani,et al.  Large scale networks fingerprinting and visualization using the k-core decomposition , 2005, NIPS.

[56]  Rossano Schifanella,et al.  The role of information diffusion in the evolution of social networks , 2013, KDD.

[57]  Nicola Perra,et al.  Modelling opinion dynamics in the age of algorithmic personalisation , 2018, Scientific Reports.

[58]  Grant Blank,et al.  The echo chamber is overstated: the moderating effect of political interest and diverse media , 2018 .

[59]  S. Fortunato,et al.  Statistical physics of social dynamics , 2007, 0710.3256.

[60]  Duncan J. Watts,et al.  Everyone's an influencer: quantifying influence on twitter , 2011, WSDM '11.

[61]  Jonathan Bright,et al.  Explaining the Emergence of Political Fragmentation on Social Media: The Role of Ideology and Extremism , 2018, J. Comput. Mediat. Commun..

[62]  Steven Walczak,et al.  Unfriending on Facebook: Friend Request and Online/Offline Behavior Analysis , 2011, 2011 44th Hawaii International Conference on System Sciences.

[63]  Feng Fu,et al.  Opinion formation on dynamic networks: identifying conditions for the emergence of partisan echo chambers , 2018, Royal Society Open Science.

[64]  J. Freedman,et al.  SELECTIVE EXPOSURE TO INFORMATION: A CRITICAL REVIEW , 1967 .

[65]  Jon M. Kleinberg,et al.  Group formation in large social networks: membership, growth, and evolution , 2006, KDD '06.

[66]  Taha Yasseri,et al.  Positive Algorithmic Bias Cannot Stop Fragmentation in Homophilic Social Networks , 2020, ArXiv.

[67]  Jon M. Kleinberg,et al.  Feedback effects between similarity and social influence in online communities , 2008, KDD.

[68]  Nitesh V. Chawla,et al.  Predictors of short-term decay of cell phone contacts in a large scale communication network , 2011, Soc. Networks.

[69]  Filippo Menczer,et al.  How algorithmic popularity bias hinders or promotes quality , 2017, Scientific Reports.

[70]  Thomas T. Hills The Dark Side of Information Proliferation , 2018, Perspectives on psychological science : a journal of the Association for Psychological Science.

[71]  Lada A. Adamic,et al.  Exposure to ideologically diverse news and opinion on Facebook , 2015, Science.

[72]  R. Durrett,et al.  Graph fission in an evolving voter model , 2012, Proceedings of the National Academy of Sciences.

[73]  Giovanni Luca Ciampaglia,et al.  A Framework for the Calibration of Social Simulation Models , 2013, Adv. Complex Syst..

[74]  Jacob Ratkiewicz,et al.  Predicting the Political Alignment of Twitter Users , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[75]  J. Cappella,et al.  Echo Chamber: Rush Limbaugh and the Conservative Media Establishment , 2008 .

[76]  Filippo Menczer,et al.  Measuring Online Social Bubbles , 2015, 1502.07162.

[77]  Haewoon Kwak,et al.  Fragile online relationship: a first look at unfollow dynamics in twitter , 2011, CHI.

[78]  Justin M. Rao,et al.  Filter Bubbles, Echo Chambers, and Online News Consumption , 2016 .

[79]  M. Macy,et al.  Complex Contagions and the Weakness of Long Ties1 , 2007, American Journal of Sociology.

[80]  Joshua M. Epstein,et al.  Growing Artificial Societies: Social Science from the Bottom Up , 1996 .

[81]  Jock Given,et al.  The wealth of networks: How social production transforms markets and freedom , 2007, Inf. Econ. Policy.

[82]  James E. Katz,et al.  Struggle in Cyberspace: Fact and Friction on the World Wide Web , 1998 .

[83]  Damon Centola An Experimental Study of Homophily in the Adoption of Health Behavior , 2011, Science.

[84]  Amos Maritan,et al.  Network model of conviction-driven social segregation. , 2018, Physical review. E.

[85]  Guillaume Deffuant,et al.  Models of Social Influence: Towards the Next Frontiers , 2017, J. Artif. Soc. Soc. Simul..

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

[87]  Xiaowen Dong,et al.  Segregation and polarization in urban areas , 2019, Royal Society Open Science.

[88]  Cun-Quan Zhang,et al.  Emergence of segregation in evolving social networks , 2011, Proceedings of the National Academy of Sciences.