Me, My Echo Chamber, and I: Introspection on Social Media Polarization

Homophily - our tendency to surround ourselves with others who share our perspectives and opinions about the world - is both a part of human nature and an organizing principle underpinning many of our digital social networks. However, when it comes to politics or culture, homophily can amplify tribal mindsets and produce "echo chambers" that degrade the quality, safety, and diversity of discourse online. While several studies have empirically proven this point, few have explored how making users aware of the extent and nature of their political echo chambers influences their subsequent beliefs and actions. In this paper, we introduce Social Mirror, a social network visualization tool that enables a sample of Twitter users to explore the politically-active parts of their social network. We use Social Mirror to recruit Twitter users with a prior history of political discourse to a randomized experiment where we evaluate the effects of different treatments on participants' i) beliefs about their network connections, ii) the political diversity of who they choose to follow, and iii) the political alignment of the URLs they choose to share. While we see no effects on average political alignment of shared URLs, we find that recommending accounts of the opposite political ideology to follow reduces participants» beliefs in the political homogeneity of their network connections but still enhances their connection diversity one week after treatment. Conversely, participants who enhance their belief in the political homogeneity of their Twitter connections have less diverse network connections 2-3 weeks after treatment. We explore the implications of these disconnects between beliefs and actions on future efforts to promote healthier exchanges in our digital public spheres.

[1]  P. Pin,et al.  Assessing the relevance of node features for network structure , 2008, Proceedings of the National Academy of Sciences.

[2]  Kevin Munger Tweetment Effects on the Tweeted: Experimentally Reducing Racist Harassment , 2017 .

[3]  Unidentified The Debunking Handbook , 2012 .

[4]  Matthew Gentzkow,et al.  Is the Internet Causing Political Polarization? Evidence from Demographics , 2017 .

[5]  J. Weibull,et al.  Political polarization , 2007, Proceedings of the National Academy of Sciences.

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

[7]  Donald P. Green,et al.  Field Experiments: Design, Analysis, and Interpretation , 2012 .

[8]  Sean A. Munson,et al.  Encouraging Reading of Diverse Political Viewpoints with a Browser Widget , 2013, ICWSM.

[9]  Stefaan Walgrave,et al.  The tie that divides: Cross‐national evidence of the primacy of partyism , 2018 .

[10]  S. Iyengar,et al.  Affect, Not Ideology A Social Identity Perspective on Polarization , 2012 .

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

[12]  Michael S. Bernstein,et al.  Designing and deploying online field experiments , 2014, WWW.

[13]  S. Iyengar,et al.  The Hostile Audience: The Effect of Access to Broadband Internet on Partisan Affect , 2017 .

[14]  Soroush Vosoughi,et al.  Twitter Demographic Classification Using Deep Multi-modal Multi-task Learning , 2017, ACL.

[15]  C. Sunstein The Law of Group Polarization , 1999, How Change Happens.

[16]  D. Murphey,et al.  The Righteous Mind: Why Good People Are Divided by Politics and Religion , 2013 .

[17]  D. Boyd,et al.  Dynamic Debates: An Analysis of Group Polarization Over Time on Twitter , 2010 .

[18]  Steven A. Sloman,et al.  Political Extremism Is Supported by an Illusion of Understanding , 2013, Psychological science.

[19]  Joshua D. Clinton,et al.  A House Divided? Roll Calls, Polarization, and Policy Differences in the U.S. House, 1877–2011 , 2017 .

[20]  Aristides Gionis,et al.  Balancing Opposing Views to Reduce Controversy , 2016, ArXiv.

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

[22]  Soroush Vosoughi,et al.  Automatic Detection and Categorization of Election-Related Tweets , 2016, ICWSM.

[23]  J. Nathan Matias,et al.  FollowBias: Supporting Behavior Change toward Gender Equality by Networked Gatekeepers on Social Media , 2017, CSCW.

[24]  Lada A. Adamic,et al.  The political blogosphere and the 2004 U.S. election: divided they blog , 2005, LinkKDD '05.

[25]  Venkata Rama Kiran Garimella,et al.  A Long-Term Analysis of Polarization on Twitter , 2017, ICWSM.

[26]  Sune Lehmann,et al.  Understanding the Demographics of Twitter Users , 2011, ICWSM.

[27]  N. Eagle,et al.  Network Diversity and Economic Development , 2010, Science.