Abstract

Social media are an important source of information about the political issues, reflecting, as well as influencing, public mood. We present an analysis of Twitter data, collected over 6 weeks before the Brexit referendum, held in the UK in June 2016. We address two questions: what is the relation between the Twitter mood and the referendum outcome, and who were the most influential Twitter users in the pro- and contra-Brexit camps? First, we construct a stance classification model by machine learning methods, and are then able to predict the stance of about one million UK-based Twitter users. The demography of Twitter users is, however, very different from the demography of the voters. By applying a simple age-adjusted mapping to the overall Twitter stance, the results show the prevalence of the pro-Brexit voters, something unexpected by most of the opinion polls. Second, we apply the Hirsch index to estimate the influence, and rank the Twitter users from both camps. We find that the most productive Twitter users are not the most influential, that the pro-Brexit camp was four times more influential, and had considerably larger impact on the campaign than the opponents. Third, we find that the top pro-Brexit communities are considerably more polarized than the contra-Brexit camp. These results show that social media provide a rich resource of data to be exploited, but accumulated knowledge and lessons learned from the opinion polls have to be adapted to the new data sources.

Highlights

  • In recent years, the use of social media has increased dramatically in the private, business, and especially political communication

  • We present an analysis of Twitter data, collected over 6 weeks before the Brexit referendum, held in the United Kingdom (UK) in June 2016

  • We address two questions: what is the relation between the Twitter mood and the referendum outcome, and who were the most influential Twitter users in the pro- and contraBrexit camps? First, we construct a stance classification model by machine learning methods, and are able to predict the stance of about one million UK-based Twitter users

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Summary

Introduction

The use of social media has increased dramatically in the private, business, and especially political communication. This is somehow surprising, given that Twitter users are in general younger voters, and in the Brexit referendum debate the majority of young voters were in favor of Remain (75–80% of voters aged 18–24) We use this result and compare it to YouGov polls on June 22, 2016 (eve-of-vote, i.e., referendum eve) and June 23, 2016 (on-the-day, i.e., referendum day) when 3766 and 4772 UK adults were asked about their voting intention and actual vote, respectively. Age-weighted poll results are considerably more (7–10%) in favor of Leave than our Twitter stance on both investigated days (eve-of-vote and on-the-day, in Tables 3 , 4, respectively) This difference suggests that the polls were underestimating the number of Brexit supporters for as much as 7–10%. The statistics portal-age distribution of Twitter users in Great Britain: https://www.statista.com/statistics/278320/age-distribution-of-twitter-users-in-great-britain/

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