Mapping social dynamics on Facebook: The Brexit debate

Abstract Nowadays users get informed and shape their opinion through social media. However, the disintermediated access to contents does not guarantee quality of information. Selective exposure and confirmation bias, indeed, have been shown to play a pivotal role in content consumption and information spreading. Users tend to select information adhering (and reinforcing) their worldview and to ignore dissenting information. This pattern elicits the formation of polarized groups – i.e., echo chambers – where the interaction with like-minded people might even reinforce polarization. In this work we address news consumption around Brexit in UK on Facebook. In particular, we perform a massive analysis on more than 1 million users interacting with Brexit related posts from the main news providers between January and July 2016. We show that consumption patterns elicit the emergence of two distinct communities of news outlets. Furthermore, to better characterize inner group dynamics, we introduce a new technique which combines automatic topic extraction and sentiment analysis. We compare how the same topics are presented on posts and the related emotional response on comments finding significant differences in both echo chambers and that polarization influences the perception of topics. Our results provide important insights about the determinants of polarization and evolution of core narratives on online debating.

[1]  Mason A. Porter,et al.  The Topological "Shape" of Brexit , 2016, ArXiv.

[2]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[3]  William M. Rand,et al.  Objective Criteria for the Evaluation of Clustering Methods , 1971 .

[4]  Nicholas A. Christakis,et al.  Cooperative behavior cascades in human social networks , 2009, Proceedings of the National Academy of Sciences.

[5]  Jon M. Kleinberg,et al.  Characterizing and curating conversation threads: expansion, focus, volume, re-entry , 2013, WSDM.

[6]  Jeffrey T. Hancock,et al.  Experimental evidence of massive-scale emotional contagion through social networks , 2014, Proceedings of the National Academy of Sciences.

[7]  Michela Del Vicario,et al.  Viral Misinformation: The Role of Homophily and Polarization , 2014, WWW.

[8]  Walter Quattrociocchi,et al.  Echo Chambers on Facebook , 2016 .

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

[10]  Jon Kleinberg,et al.  Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter , 2011, WWW.

[11]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  Guido Caldarelli,et al.  Science vs Conspiracy: Collective Narratives in the Age of Misinformation , 2014, PloS one.

[13]  Philip N. Howard,et al.  Bots, #StrongerIn, and #Brexit: Computational Propaganda during the UK-EU Referendum , 2016, ArXiv.

[14]  Guido Caldarelli,et al.  Users Polarization on Facebook and Youtube , 2016, PloS one.

[15]  Guido Caldarelli,et al.  Opinion dynamics on interacting networks: media competition and social influence , 2014, Scientific Reports.

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

[17]  Damon Centola,et al.  The Spread of Behavior in an Online Social Network Experiment , 2010, Science.

[18]  Marián Boguñá,et al.  Extracting the multiscale backbone of complex weighted networks , 2009, Proceedings of the National Academy of Sciences.

[19]  Réka Albert,et al.  Near linear time algorithm to detect community structures in large-scale networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  N. Newman,et al.  Reuters Institute Digital News Report 2019 , 2019 .

[21]  Davide Emilio Galli,et al.  Brexit or Bremain? Evidence from Bubble Analysis , 2016, MIDAS@PKDD/ECML.

[22]  Aldo Gangemi,et al.  A Comparison of Knowledge Extraction Tools for the Semantic Web , 2013, ESWC.

[23]  Guido Caldarelli,et al.  Debunking in a world of tribes , 2015, PloS one.

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

[25]  Guido Caldarelli,et al.  Emotional Dynamics in the Age of Misinformation , 2015, PloS one.

[26]  Camille Roth,et al.  Natural Scales in Geographical Patterns , 2017, Scientific Reports.

[27]  Bruno Pouliquen,et al.  An introduction to the Europe Media Monitor family of applications , 2013, ArXiv.

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

[29]  Ee-Peng Lim,et al.  Finding Bursty Topics from Microblogs , 2012, ACL.