Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Adamopoulos P, Todri V (2015) The effectiveness of marketing strategies in social media. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, KDD’15, pp 1641–1650. https://doi.org/10.1145/2783258.2788597
Adler-Nissen R, Galpin C, Rosamond B (2017) ‘Performing Brexit: how a post-Brexit world is imagined outside the United Kingdom’. Br J Polit Int Relat 19(3):573–591. https://doi.org/10.1177/1369148117711092
Ball R, Brown P (1968) An empirical evaluation of accounting income numbers. J Account Res 6:159. https://doi.org/10.2307/2490232
Barberá P, Jost JT, Nagler J et al (2015) Tweeting from left to right: is online political communication more than an echo chamber? Psychol Sci 26:1531–1542. https://doi.org/10.1177/0956797615594620
Berger PL, Luckmann T (1966) The social construction of reality a treatise in the sociology of knowledge. Doubleday & Company, New York
Bina C, Vo M (2007) OPEC in the epoch of globalization: an event study of global oil prices. Glob Econ J 7:2
Binder J (1998) The event study methodology since 1969. Rev Quant Financ Account 11:111–137. https://doi.org/10.1023/A:1008295500105
Blansky D, Kavanaugh C, Boothroyd C et al (2013) Spread of academic success in a high school social network. PLoS One 8:e55944. https://doi.org/10.1371/journal.pone.0055944
Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 2008:1–12. https://doi.org/10.1088/1742-5468/2008/10/P10008
Bosch T (2017) Twitter activism and youth in South Africa: the case of #RhodesMustFall. Inf Commun Soc 20:221–232. https://doi.org/10.1080/1369118X.2016.1162829
Bossetta M, Dutceac Segesten A, Trenz H-J (2017) Engaging with European politics through Twitter and Facebook: participation beyond the national? In: Social media and European politics. Palgrave Macmillan, London, pp 53–76
Brady WJ, Wills JA, Jost JT et al (2017) Emotion shapes the diffusion of moralized content in social networks. Proc Natl Acad Sci 114:7313–7318. https://doi.org/10.1073/pnas.1618923114
Burnap P, Gibson R, Sloan L et al (2016) 140 characters to victory?: using Twitter to predict the UK 2015 general election. Elect Stud 41:230–233. https://doi.org/10.1016/j.electstud.2015.11.017
Castellano C, Pastor-Satorras R (2010) Thresholds for epidemic spreading in networks. Phys Rev Lett 105:218701. https://doi.org/10.1103/PhysRevLett.105.218701
Castells M (2007) Communication, power and counter-power in the network society. Int J Commun 1:238–266
Chen D-B, Gao H, Lü L, Zhou T (2013) Identifying influential nodes in large-scale directed networks: the role of clustering. PLoS One 8:e77455. https://doi.org/10.1371/journal.pone.0077455
Chiu S-I, Hsu K-W (2017) Information diffusion on Facebook: a case study of the sunflower student movement in Taiwan. In: Proceedings of the 11th international conference on ubiquitous information management and communication—IMCOM’17. ACM Press, New York, pp 1–8
Comin CH, da Fontoura Costa L (2011) Identifying the starting point of a spreading process in complex networks. Phys Rev E 84:056105. https://doi.org/10.1103/PhysRevE.84.056105
Correa JC, Camargo JE (2017) Ideological consumerism in Colombian elections, 2015: links between political ideology, Twitter activity, and electoral results. Cyberpsychol Behav Soc Netw 20:37–43. https://doi.org/10.1089/cyber.2016.0402
Csaba L (2016) The EU at sixty: a watershed or business as usual? (comparing Grexit to Brexit). Acta Oecon 66:61–75. https://doi.org/10.1556/032.2016.66.S1.4
Diamond P, Nedergaard P, Rosamond B (eds) (2019) Routledge handbook of the politics of Brexit. Routledge, London
Dolley JC (1933) Characteristics and procedure of common stock split-ups. Harv Bus Rev 316–326
Dybiec B, Mitarai N, Sneppen K (2012) Information spreading and development of cultural centers. Phys Rev E 85:056116. https://doi.org/10.1103/PhysRevE.85.056116
Engle R (2001) GARCH 101: the use of ARCH/GARCH models in applied econometrics. J Econ Perspect 15:157–168. https://doi.org/10.1257/jep.15.4.157
Enli G, Simonsen CA (2018) ‘Social media logic’ meets professional norms: Twitter hashtags usage by journalists and politicians. Inf Commun Soc 21:1081–1096. https://doi.org/10.1080/1369118X.2017.1301515
Ernst N, Engesser S, Büchel F et al (2017) Extreme parties and populism: an analysis of Facebook and Twitter across six countries. Inf Commun Soc 20:1347–1364. https://doi.org/10.1080/1369118X.2017.1329333
Fama EF, Fisher L, Jensen MC, Roll R (1969) The adjustment of stock prices to new information. Int Econ Rev (Philadelphia) 10:1. https://doi.org/10.2307/2525569
Fiske J, Hartley J (2003) Reading television. Routledge, London
Garimella K, Weber I (2017) A long-term analysis of polarization on Twitter
Grčar M, Cherepnalkoski D, Mozetič I, Kralj Novak P (2016) Brexit stance annotated tweets. In: Slovenian language resource repository CLARIN.SI. http://hdl.handle.net/11356/1135
Grčar M, Cherepnalkoski D, Mozetič I, Kralj Novak P (2017) Stance and influence of Twitter users regarding the Brexit referendum. Comput Soc Netw 4:6. https://doi.org/10.1186/s40649-017-0042-6
Guille A, Hacid H (2012) Predictive model for the temporal dynamics of information diffusion in online social networks. Int Work Min Soc Netw Dyn. https://doi.org/10.1145/2187980.2188254
Guille A, Hacid H, Favre C, Zighed DA (2013) Information diffusion in online social networks: a survey. SIGMOD Rec 42:17–28. https://doi.org/10.1145/2503792.2503797
Halberstam Y, Knight B (2016) Homophily, group size, and the diffusion of political information in social networks: evidence from Twitter. J Public Econ 143:73–88. https://doi.org/10.1016/j.jpubeco.2016.08.011
Hay C, Rosamond B (2002) Globalisation, European integration and the discursive construction of economic imperatives. J Eur Public Policy 9(2):147–167
Howard PN, Kollanyi B (2016) Bots, #StrongerIn, and #Brexit: computational propaganda during the UK-EU referendum. SSRN Electron J. https://doi.org/10.2139/ssrn.2798311
Howard PN, Bradshaw S, Kollanyi B et al (2017) Junk news and bots during the French presidential election: what are French voters sharing over Twitter? In: ComProp data memo, pp 1–5
Hwang S (2013) The effect of Twitter use on politicians’ credibility and attitudes toward politicians. J Public Relat Res. https://doi.org/10.1080/1062726X.2013.788445
Iribarren JL, Moro E (2009) Impact of human activity patterns on the dynamics of information diffusion. Phys Rev Lett 103:038702. https://doi.org/10.1103/PhysRevLett.103.038702
Iribarren JL, Moro E (2011) Branching dynamics of viral information spreading. Phys Rev E 84:046116. https://doi.org/10.1103/PhysRevE.84.046116
Kim J, Hastak M (2018) Social network analysis: characteristics of online social networks after a disaster. Int J Inf Manag 38:86–96. https://doi.org/10.1016/J.IJINFOMGT.2017.08.003
Kitsak M, Gallos LK, Havlin S et al (2010) Identification of influential spreaders in complex networks. Nat Phys 6:888–893. https://doi.org/10.1038/nphys1746
Kossinets G, Kleinberg J, Watts D (2008) The structure of information pathways in a social communication network. In: Proceeding of the 14th ACM SIGKDD international conference on knowledge discovery and data mining—KDD 08. ACM Press, New York, p 435
Kruikemeier S, van Noort G, Vliegenthart R, de Vreese CH (2013) Getting closer: the effects of personalized and interactive online political communication. Eur J Commun 28:53–66. https://doi.org/10.1177/0267323112464837
Li D, Zhang S, Sun X et al (2017) Modeling information diffusion over social networks for temporal dynamic prediction. IEEE Trans Knowl Data Eng 29:1985–1997
Liu C, Zhan XX, Zhang ZK et al (2015) How events determine spreading patterns: Information transmission via internal and external influences on social networks. New J Phys. https://doi.org/10.1088/1367-2630/17/11/113045
Lytras M, Visvizi A, Damiani E, Mathkour H (2019) The cognitive computing turn in education: prospects and application. Comput Hum Behav 92:446–449. https://doi.org/10.1016/J.CHB.2018.11.011
MacKinlay AC (1997) Event studies in economics and finance. J Econ Lit 35:13–39. https://doi.org/10.2307/2729691
Marine-Roig E, Martin-Fuentes E, Daries-Ramon N (2017) User-generated social media events in tourism. Sustain. https://doi.org/10.3390/su9122250
Mcgregor SC, Mourão RR, Molyneux L (2017) Twitter as a tool for and object of political and electoral activity: considering electoral context and variance among actors. https://doi.org/10.1080/19331681.2017.1308289
Mellon J, Prosser C (2017) Twitter and Facebook are not representative of the general population: political attitudes and demographics of British social media users. Res Polit 4:205316801772000. https://doi.org/10.1177/2053168017720008
Menon K, Kärkkäinen H, Jussila J et al (2018) Analysing the role of crowdfunding in entrepreneurial ecosystems: a social media event study of two competing product launches. Int J Entrep Small Bus 33:575–606
Milliken J (1999) The study of discourse in international relations: a critique of research and methods. Eur J Int Relat 5(2):225–254
Mønsted B, Sapieżyński P, Ferrara E, Lehmann S (2017) Evidence of complex contagion of information in social media: an experiment using Twitter bots. PLoS One 12:e0184148. https://doi.org/10.1371/journal.pone.0184148
Myers SA, Zhu C, Leskovec J (2012) Information diffusion and external influence in networks. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining—KDD’12. ACM Press, New York, p 33
Nagata K, Shirayama S (2012) Method of analyzing the influence of network structure on information diffusion. Phys A Stat Mech Appl 391:3783–3791. https://doi.org/10.1016/J.PHYSA.2012.02.031
Nematzadeh A, Ferrara E, Flammini A, Ahn Y-Y (2014) Optimal network modularity for information diffusion. Phys Rev Lett 113:088701. https://doi.org/10.1103/PhysRevLett.113.088701
Newman TP (2017) Tracking the release of IPCC AR5 on Twitter: users, comments, and sources following the release of the Working Group I summary for policymakers. Public Underst Sci 26:815–825. https://doi.org/10.1177/0963662516628477
Niederhoffer V (1971) The analysis of world events and stock prices. J Bus 44:193–219. https://doi.org/10.1086/295352
Ott BL (2017) The age of Twitter: Donald J. Trump and the politics of debasement. Crit Stud Media Commun 34:59–68. https://doi.org/10.1080/15295036.2016.1266686
Pastor-Satorras R, Vespignani A (2001) Epidemic spreading in scale-free networks. Phys Rev Lett 86:3200–3203. https://doi.org/10.1103/PhysRevLett.86.3200
Peres R (2014) The impact of network characteristics on the diffusion of innovations. Phys A Stat Mech Appl 402:330–343. https://doi.org/10.1016/J.PHYSA.2014.02.003
Pinto PC, Thiran P, Vetterli M (2012) Locating the source of diffusion in large-scale networks. Phys Rev Lett 109:068702. https://doi.org/10.1103/PhysRevLett.109.068702
Ramos-Serrano M, Fernández Gómez JD, Pineda A (2018) ‘Follow the closing of the campaign on streaming’: the use of Twitter by Spanish political parties during the 2014 European elections. New Media Soc 20:122–140. https://doi.org/10.1177/1461444816660730
Reichardt I (2016) ‘Russian propaganda in the West’, yearbook of the Institute of East-Central Europe, vol 14(2), pp 9–22
Romero DM, Meeder B, Kleinberg J (2011) Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In: Proceedings of the 20th international conference on world wide web—WWW’11. ACM Press, New York, p 695
Rosamond B (2001) Constructing globalization. In: Fierke KM, Jørgensen KE (eds) Constructing international relations: the next generation. International relations in a constructed world. M.E.Sharpe, Armonk, pp 201–219
Rosamond B (2016) Brexit and the problem of European disintegration. J Contemp Eur Res 12(4):864–871
Ruggie JG (1998) ‘What makes the world hang together? Neoutilitarianism and the social constructivist challenge’. Int Org 52(4):855–885
Sang E, Bos J (2012) Predicting the 2011 Dutch senate election results with Twitter. In: Proceedings of the workshop on semantic analysis, pp 53–60
Shadurski V, Malishevskaya G (2019) Contemporary politics and society: social media and public engagement in Belarus. In: Visvizi A, Lytras M (eds) (2019) Politics and technology in the post-truth era. Emerald Publishing, Bingley
Shen Z, Wang W-X, Fan Y et al (2014) Reconstructing propagation networks with natural diversity and identifying hidden sources. Nat Commun 5:4323. https://doi.org/10.1038/ncomms5323
Siikanen M, Baltakys K, Kanniainen J et al (2018) Facebook drives behavior of passive households in stock markets. Financ Res Lett 27:208–213. https://doi.org/10.1016/J.FRL.2018.03.020
Singh H, Bagchi A, Singla P (2017) Learning user representations in online social networks using temporal dynamics of information diffusion
Stieglitz S, Dang-Xuan L (2012) Political communication and influence through microblogging—an empirical analysis of sentiment in Twitter messages and retweet behavior. Proc Annu Hawaii Int Conf Syst Sci 3500–3509. https://doi.org/10.1109/HICSS.2012.476
Toriz Ramos C (2019) From the freedom of the press to the freedom of the internet: a new public sphere in the making?. In: Visvizi A, Lytras M (eds) (2019) Politics and technology in the post-truth era. Emerald Publishing, Bingley
Tumasjan A, Sprenger T, Sandner P, Welpe I (2010) Predicting elections with Twitter: what 140 characters reveal about political sentiment. In: Proceedings of the fourth international AAAI conference on weblogs social media, pp 178–185. https://doi.org/10.1074/jbc.M501708200
Vespignani A (2009) Predicting the behavior of techno-social systems. Science 325:425–428. https://doi.org/10.1126/science.1171990
Visvizi A, Lytras M (eds) (2019) Politics and technology in the post-truth era. Emerald Publishing, Bingley
Wattal S, Schuff D, Mandviwalla M, Williams CB (2010) Web 2.0 and politics: the 2008 U.S. presidential election and an E-Politics research agenda. MIS Q 34:669. https://doi.org/10.2307/25750700
Wigand FDL (2011) Tweets and retweets: Twitter takes wing in government. Inf Polity 16:215–224. https://doi.org/10.3233/IP-2011-0241
Ye S, Wu SF (2010) Measuring message propagation and social influence on Twitter.com. In: Lecture notes in computer science, pp 216–231
Youmans WL, York JC (2012) Social Media and the activist toolkit: user agreements, corporate interests, and the information infrastructure of modern social movements. J Commun 62:315–329. https://doi.org/10.1111/j.1460-2466.2012.01636.x
Zaharia M (2015) Greek failure to pay official lenders could trigger CDS payments-lawyers, Reuters, Bond News. https://www.reuters.com/article/markets-greece-cds-idUSL5N0VL2FK20150212. Accessed 12 Feb 2015
Zhang Y, Zhou S, Zhang Z et al (2013) Rumor evolution in social networks. Phys Rev E 87:032133. https://doi.org/10.1103/PhysRevE.87.032133
Zhou Y, Xia Y (2014) Epidemic spreading on weighted adaptive networks. Phys A Stat Mech Appl 399:16–23. https://doi.org/10.1016/J.PHYSA.2013.12.036
Zinoviev D (2012) Information diffusion in social networks. In: Social networking and community behavior modeling: qualitative and quantitative measures. IGI Global, pp 146–163
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Mora-Cantallops, M., Sánchez-Alonso, S. & Visvizi, A. The influence of external political events on social networks: the case of the Brexit Twitter Network. J Ambient Intell Human Comput 12, 4363–4375 (2021). https://doi.org/10.1007/s12652-019-01273-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-019-01273-7