Abstract
User generated media, and their influence on the information individuals are exposed to, have the potential to affect political outcomes. This is increasingly a focus for attention and concern. The British EU membership referendum provided an opportunity for researchers to explore the nature and impact of the new infosphere in a politically charged situation. This work contributes by reviewing websites that were linked in a Brexit Tweet dataset of 13.2 million tweets, by 1.8 million distinct users, collected in the run-up to the referendum. In this dataset, 480,000 users have been classified according to their “Brexit” vote intent. Findings include that linked material on Twitter was mostly posted by those in favour of leaving the EU. Mainstream news media had the greatest impact in terms of number of links tweeted, with alternative media and campaign sites appearing to a much lesser extent. Of the 15 most linked mainstream media, half show a substantially greater appeal to the leave camp, with two of them very much so. No mainstream media had a consistent appeal among remain supporters. Among the sites that were highly favoured by one voter valence or the other, the leave sites had by far the greatest impact in terms of number of appearances in tweets. Remain-preferred sites were less linked, and dominated by explicit campaign sites. Leave-preferred sites were more numerously linked, and dominated by mainstream and alternative media.
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Acknowledgments
This work was partially supported by the European Union under grant agreements No. 687847 “Comrades” and No. 654024 “SoBigData”, the UK Engineering and Physical Sciences Research Council grant EP/I004327/1 and the British Academy under call “The Humanities and Social Sciences Tackling the UKs International Challenges”.
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Gorrell, G., Roberts, I., Greenwood, M.A., Bakir, M.E., Iavarone, B., Bontcheva, K. (2018). Quantifying Media Influence and Partisan Attention on Twitter During the UK EU Referendum. In: Staab, S., Koltsova, O., Ignatov, D. (eds) Social Informatics. SocInfo 2018. Lecture Notes in Computer Science(), vol 11185. Springer, Cham. https://doi.org/10.1007/978-3-030-01129-1_17
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