The influence of external political events on social networks: the case of the Brexit Twitter Network

The social media debate preceding the 2016 Brexit referendum represents a yet another instance of the growing role of social networking sites, also known as social media, in steering the dynamics of the socio-political process nowadays. Considering the scale of the phenomenon as well as the variety of concerns it raises vis-à-vis transparency, equality, representation, and legitimacy of the political process, it is imperative to query the mechanisms behind the relationship that unfolds between social media, their users and the political process. To do this, this paper employs event study analysis and recent advances in data mining and data analysis to examine how certain non-virtual political events constituent of the Brexit debate had been played out in the social media realm and influenced the social media users’ stance toward the very question of Brexit. This composite methodological approach that this study adopts allows to measure how non-virtual political events influenced the network of users who discussed the withdrawal of the UK from the EU in Twitter in the weeks prior to the Brexit referendum. The outcomes of this study suggest that social networking sites play a pivotal role not only on how information is diffused over the network but also on user’s message creation, dissemination behaviour and the shape of the social network itself.

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

[2]  L. D. Costa,et al.  Identifying the starting point of a spreading process in complex networks. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  Makarand Hastak,et al.  Social network analysis: Characteristics of online social networks after a disaster , 2018, Int. J. Inf. Manag..

[4]  M. Vo,et al.  OPEC in the Epoch of Globalization: An Event Study of Global Oil Prices , 2007 .

[5]  Logan Molyneux,et al.  Twitter as a tool for and object of political and electoral activity: Considering electoral context and variance among actors , 2017 .

[6]  Parag Singla,et al.  Learning User Representations in Online Social Networks using Temporal Dynamics of Information Diffusion , 2017, ArXiv.

[7]  Jillian C. York,et al.  Social Media and the Activist Toolkit: User Agreements, Corporate Interests, and the Information Infrastructure of Modern Social Movements , 2012 .

[8]  Kim Sneppen,et al.  Information spreading and development of cultural centers , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  P. Berger,et al.  The Social Construction of Reality , 1966 .

[10]  E. Fama,et al.  The Adjustment of Stock Prices to New Information , 1969 .

[11]  Shu-I Chiu,et al.  Information diffusion on Facebook: a case study of the sunflower student movement in Taiwan , 2017, IMCOM.

[12]  Isabell M. Welpe,et al.  Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment , 2010, ICWSM.

[13]  Esteban Moro,et al.  Impact of human activity patterns on the dynamics of information diffusion. , 2009, Physical review letters.

[14]  Igor Mozetič,et al.  Stance and influence of Twitter users regarding the Brexit referendum , 2017, Computational social networks.

[15]  Manuel Castells,et al.  Communication Power and Counter-power in the Network Society , 2007 .

[16]  Jure Leskovec,et al.  Information diffusion and external influence in networks , 2012, KDD.

[17]  Lev Muchnik,et al.  Identifying influential spreaders in complex networks , 2010, 1001.5285.

[18]  Jorge E. Camargo,et al.  Ideological Consumerism in Colombian Elections, 2015: Links Between Political Ideology, Twitter Activity, and Electoral Results , 2016, Cyberpsychology Behav. Soc. Netw..

[19]  Cláudia Toriz Ramos Chapter 2 From the Freedom of the Press to the Freedom of the Internet: A New Public Sphere in the Making? , 2019, Politics and Technology in the Post-Truth Era.

[20]  Johan Bos,et al.  Predicting the 2011 Dutch Senate Election Results with Twitter , 2012 .

[21]  R. Engle GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics , 2001 .

[22]  Renana Peres,et al.  The impact of network characteristics on the diffusion of innovations , 2014 .

[23]  Todd P. Newman Tracking the release of IPCC AR5 on Twitter: Users, comments, and sources following the release of the Working Group I Summary for Policymakers , 2017, Public understanding of science.

[24]  Jon M. Kleinberg,et al.  The structure of information pathways in a social communication network , 2008, KDD.

[25]  Yingjie Xia,et al.  Epidemic spreading on weighted adaptive networks , 2014 .

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

[27]  Estela Marine-Roig,et al.  User-Generated Social Media Events in Tourism , 2017 .

[28]  Shyhtsun Felix Wu,et al.  Measuring message propagation and social influence on Twitter.com , 2010, Int. J. Commun. Networks Distributed Syst..

[29]  Claudio Castellano,et al.  Thresholds for epidemic spreading in networks , 2010, Physical review letters.

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

[31]  Victor Niederhoffer,et al.  The Analysis of World Events and Stock Prices , 1971 .

[32]  Cécile Favre,et al.  Information diffusion in online social networks: a survey , 2013, SGMD.

[33]  Marina Ramos-Serrano,et al.  ‘Follow the closing of the campaign on streaming’: The use of Twitter by Spanish political parties during the 2014 European elections , 2018, New Media Soc..

[34]  E. Moro,et al.  Information Diffusion Epidemics in Social Networks , 2007, 0706.0641.

[35]  J. Milliken The Study of Discourse in International Relations: , 1999 .

[36]  Esteban Moro Egido,et al.  Branching Dynamics of Viral Information Spreading , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[37]  Ben Rosamond,et al.  Globalization, European integration and the discursive construction of economic imperatives , 2002 .

[38]  Wen-Xu Wang,et al.  Reconstructing propagation networks with natural diversity and identifying hidden sources , 2014, Nature Communications.

[39]  B. Rosamond,et al.  The Routledge Handbook of the Politics of Brexit , 2018 .

[40]  Stefan Stieglitz,et al.  Political Communication and Influence through Microblogging--An Empirical Analysis of Sentiment in Twitter Messages and Retweet Behavior , 2012, 2012 45th Hawaii International Conference on System Sciences.

[41]  B. Rosamond Brexit and the Problem of European Disintegration , 2016, Journal of Contemporary European Research.

[42]  A. Mackinlay,et al.  Event Studies in Economics and Finance , 1997 .

[43]  Rens Vliegenthart,et al.  Getting closer: The effects of personalized and interactive online political communication , 2013 .

[44]  Joshua A. Tucker,et al.  Emotion shapes the diffusion of moralized content in social networks , 2017, Proceedings of the National Academy of Sciences.

[45]  Hakim Hacid,et al.  A predictive model for the temporal dynamics of information diffusion in online social networks , 2012, WWW.

[46]  Anamaria Dutceac Segesten,et al.  Engaging with European Politics Through Twitter and Facebook: Participation Beyond the National? , 2017 .

[47]  Dianne Lux Wigand,et al.  Tweets and retweets: Twitter takes wing in government , 2011, Inf. Polity.

[48]  Tiago Braga,et al.  Serglycin-deficient Cytotoxic T Lymphocytes Display Defective Secretory Granule Maturation and Granzyme B Storage* , 2005, Journal of Biological Chemistry.

[49]  J. Kanniainen,et al.  Facebook Drives Behavior of Passive Households in Stock Markets , 2017, Finance Research Letters.

[50]  Anna Visvizi Politics and Technology in the Post-Truth Era , 2019 .

[51]  Martin Vetterli,et al.  Locating the Source of Diffusion in Large-Scale Networks , 2012, Physical review letters.

[52]  G. Enli,et al.  ‘Social media logic’ meets professional norms: Twitter hashtags usage by journalists and politicians , 2017 .

[53]  J. Ruggie What Makes the World Hang Together? Neo-utilitarianism and the Social Constructivist Challenge , 1998, International Organization.

[54]  Alessandro Vespignani,et al.  Epidemic spreading in scale-free networks. , 2000, Physical review letters.

[55]  V. Shadurski,et al.  Chapter 4 Contemporary Politics and Society: Social Media and Public Engagement in Belarus , 2019, Politics and Technology in the Post-Truth Era.

[56]  Brian G. Knight,et al.  Homophily, Group Size, and the Diffusion of Political Information in Social Networks: Evidence from Twitter , 2014 .

[57]  Joshua A. Tucker,et al.  Is Online Political Communication More Than an Echo Chamber? , 2022 .

[58]  Katsuya Nagata,et al.  Method of analyzing the influence of network structure on information diffusion , 2012 .

[59]  Jonathan Mellon,et al.  Twitter and Facebook are not representative of the general population: Political attitudes and demographics of British social media users , 2017 .

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

[61]  Panagiotis Adamopoulos,et al.  The Effectiveness of Marketing Strategies in Social Media: Evidence from Promotional Events , 2015, KDD.

[62]  Chuang Liu,et al.  How events determine spreading patterns: information transmission via internal and external influences on social networks , 2015, ArXiv.

[63]  Christine B. Williams,et al.  Web 2.0 and Politics: The 2008 U.S. Presidential Election and an E-Politics Research Agenda , 2010, MIS Q..

[64]  Piotr Sapiezynski,et al.  Evidence of complex contagion of information in social media: An experiment using Twitter bots , 2017, PloS one.

[65]  Tanja Bosch,et al.  Twitter activism and youth in South Africa: the case of #RhodesMustFall , 2017 .

[66]  Rebecca Adler-Nissen,et al.  Performing Brexit: How a post-Brexit world is imagined outside the United Kingdom , 2017 .

[67]  R. Ball,et al.  An empirical evaluation of accounting income numbers , 1968 .

[68]  Miltiadis D. Lytras,et al.  The cognitive computing turn in education: Prospects and application , 2019, Comput. Hum. Behav..

[69]  Alessandro Flammini,et al.  Optimal network clustering for information diffusion , 2014, Physical review letters.

[70]  Dmitry Zinoviev Information Diffusion in Social Networks , 2012 .

[71]  Rachel Gibson,et al.  140 Characters to Victory?: Using Twitter to Predict the UK 2015 General Election , 2015, ArXiv.

[72]  John J. Binder The Event Study Methodology Since 1969 , 1997 .

[73]  Hiroki Sayama,et al.  Spread of Academic Success in a High School Social Network , 2013, PloS one.

[74]  Brian L. Ott The age of Twitter: Donald J. Trump and the politics of debasement , 2017 .

[75]  Ravikiran Vatrapu,et al.  Analysing the Role of Crowdfunding in Entrepreneurial Ecosystems: A Social Media Event Study of Two Competing Product Launches , 2018 .

[76]  A. Vespignani Predicting the Behavior of Techno-Social Systems , 2009, Science.

[77]  Sven Engesser,et al.  Extreme parties and populism: an analysis of Facebook and Twitter across six countries , 2017 .

[78]  Sungwook Hwang The Effect of Twitter Use on Politicians’ Credibility and Attitudes toward Politicians , 2013 .

[79]  Xuelong Li,et al.  Modeling Information Diffusion over Social Networks for Temporal Dynamic Prediction , 2017, IEEE Trans. Knowl. Data Eng..

[80]  Hui Gao,et al.  Identifying Influential Nodes in Large-Scale Directed Networks: The Role of Clustering , 2013, PloS one.