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Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case

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In this study, we demonstrate that the computational social science is important to understand people behavior in political phenomena, and based on the long-running Brexit debate analysis on Twitter, we predict the public stance, discussion topics, and we measure the involvement of automated accounts and politicians’ social media accounts.

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Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case

  1. 1. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case Marco Brambilla, Emre Calisir @marcobrambi Politecnico di Milano, Data Science Lab Amsterdam July 18 2019
  2. 2. The New Agora • Social media platforms are the new places for political debate Brambilla, Calisir. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case.
  3. 3. Political Discussions on Social Media Not only drama (uprisings, riots, ..) ◢US Presidency (Obama, Trump) ◢National elections throughout the world ◢Referendums (Catalunya, Lombardia, Brexit) ◢Political agenda (immigration, education, ..) What can you find there? ◢ Direct debates between candidates ◢ Criticisms and defense of political actions ◢ Discussions between voters ◢Echo of events and facts Brambilla, Calisir. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case.
  4. 4. A Comprehensive Approach to the Phenomenon ◢Key Events ◢Topics ◢Main Actors ◢Audience opinion ◢Sentiment ◢Spreading of info ◢Robotization ◢Dynamic aspects Stance Topic Discovery Top Influencers Temporal Analysis Demographics Sentiment Brambilla, Calisir. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case.
  5. 5. BREXIT Research Questions • How can we analyze polarized political elections such as referendums with social media data? • How public mood is changed before and after the referendum? • What is the influence of politicians to the online debate? • What is the influence of automated accounts to the online debate? Brambilla, Calisir. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case.
  6. 6. Twitter. Not just because … Statistics ◢800 mln monthly active users ◢500 mln daily active users Features ◢People tend to share political opinions ◢Information spreading speed ◢Public tweets Text content Mentions, hashtags, retweets, likes Brambilla, Calisir. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case.
  7. 7. Twitter. Not just because … 10M+ tweets containing Brexit keyword Brambilla, Calisir. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case.
  8. 8. Analysis setting • Collection of tweets containing the keyword Brexit • neutrality of the term proven by empirical studies • between January 2016 and October 2018 (.. But ongoing) • using Twitter API • 10 million tweets sent by 1.5 million users • multi-language Brambilla, Calisir. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case.
  9. 9. Some stats So many things in heaven and earth, Oratio.
  10. 10. User Participation to Twitter Debate • Limited attention span 56% of users tweeted using Brexit keyword only once in the 28 months of our analysis Brambilla, Calisir. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case.
  11. 11. The Language of Brexit 81% of tweets are posted in English. Tweets written in French and Spanish, Italian and German around 2-4% Brambilla, Calisir. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case.
  12. 12. Geo-Location of Brexit Tweets There is an interest from all over the world, while not surprisingly most of them are posted from UK. Brambilla, Calisir. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case. Basedongeotaggedposts around 45% from the UK
  13. 13. Demographics: Gender of users Gender distribution is very similar to Twitter population Infoextractedfromprofilephotos(around30%ofdata) Brambilla, Calisir. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case.
  14. 14. Age of users Users who discuss Brexit are older than Twitter distribution. Scarce attention from young people. Infoextractedfromprofilephotos(around30%ofdata) Brambilla, Calisir. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case.
  15. 15. Temporal Analysis to detect key events Daily unique users participating to Brexit debate 1 2,3 4,5 6,7,8 109 Vote BrexitProcess start Trum p elected M ay’s speechBrexitBill& Article 50 N egotiations start Firstdealfails Brambilla, Calisir. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case.
  16. 16. Daily Increases > 150% (Daily) Mapping with Key Event 1 June 24, 2016 UK votes to leave EU 2 Nov 3, 2016 Parliament must vote on whether the UK can start the process of leaving the EU, the High Court has ruled. 3 Nov 9, 2016 Donald Trump is selected as US president, and European commission congratulated Trump’s victory. 4 Jan 17, 2017 Theresa May’s Brexit speech 5 Jan 24, 2017 The Supreme Court is ruling Brexit delivery 6 Mar 13, 2017 Parliament passes Brexit bill and opens way to triggering article 50 7 Mar 29, 2017 Prime Minister triggers Article 50 of the Treaty on European Union 8 Apr 18, 2017 Prime Minister calls a General Election – to be held on 8 June 2017. 9 June 19, 2017 UK-EU exit negotiations start 10 Dec 4, 2017 UK and EU fail to strike Brexit talks deal Corresponding Events Brambilla, Calisir. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case.
  17. 17. Recent Brexit tweets are more influential Avg favorites Avg retweets Brambilla, Calisir. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case.
  18. 18. Stance I’m not against your idea, I’m in favor of mine
  19. 19. User Stance: syntactical rule-based approach • Polarized hastags Opinion Hashtags Occurrence Neutral #brexit, #euref, #eureferendum 8.7M Pro-remain #strongerin, #voteremain, #intogether, #labourinforbritain, #moreincommon, #greenerin, #catsagainstbrexit, #bremain, #betteroffin, #leadnotleave, #remain, #stay, #ukineu, #votein, #voteyes, #yes2eu, #yestoeu, #sayyes2europe 354K Pro-leave #independenceday , #leaveeuofficial , #leaveeu , #leave , ##labourleave , #votetoleave , #voteleave , #takebackcontrol , #ivotedleave , #beleave , #betteroffout , #britainout , #nottip , #takecontrol , #voteno , #voteout , ##voteleaveeu 651K Brambilla, Calisir. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case.
  20. 20. User Stance evolution in time Brambilla, Calisir. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case. Based on use of polarized hashtags
  21. 21. Supervised Learning Pipeline for Stance Classification Human-in-the-loop paradigm Training from human-annotated and rule-based training Training Dataset Balanced tweets from Remain Leave and Neutral Feature Engineering + Transformations Learning Model SVM, Log Regr. Random Forest, LSTM Evaluation Adjustments Brambilla, Calisir. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case.
  22. 22. Increasing engagement of pro-remain right after the referendum Increase in Pro-Remain Brambilla, Calisir. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case.
  23. 23. Increasing engagement of pro-leave later Increase in Pro-Leave Brambilla, Calisir. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case.
  24. 24. Stance in time (%) on the whole dataset Brambilla, Calisir. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case.
  25. 25. Another Perspective: Is there any change in stance after the referendum? 62% of Pro-Leavers moved to Pro-Remain stance after the referendum Brambilla, Calisir. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case.
  26. 26. Top influencers are the accounts that other users discussed about Politicians News Channels Campaign Accounts @UKLabour 44K @Conservatives 31K @LeaveEUOfficial 25K @vote_leave 24K @UKIP 18K @LibDems 16K @StrongerIn 16K @theresa_may 68K @Nigel_Farage 53K @jeremycorbyn 44K @BorisJohnson 41K @David_Cameron 33K @realDonaldTrump 30K @DavidDavisMP 19K @NicolaSturgeon 11K @JunckerEU 11K @MichelBarnier 10K @ChukaUmunna 10K @BBCNews 39K @SkyNews 28K @guardian 28K @FT 27K @LBC 22K @Independent 15K @Telegraph 13K @afneil 11K @MailOnline 10K Top mentioned accounts Brambilla, Calisir. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case.
  27. 27. Temporal and comparative analysis of Brexit tweets corresponding to politician names Handing over PM from Cameron to Theresa, the visit of Trump... Brambilla, Calisir. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case.
  28. 28. Adding sentiment… • Stance and sentiment (may be) orthogonal Brambilla, Calisir. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case.
  29. 29. Analysis of top mentioned politician accounts in stance and sentiment dimensions Politician who is discussed most positively: Donald Trump and Nigel Farage Politician who is discussed most negatively: David Cameron and Jeremy Corbyn Pro-remainers discuss more about Boris Johnson Brambilla, Calisir. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case.
  30. 30. Topic Analysis + User Stance + Tweet Sentiment (2018) [part 1] Topic Stance Sentiment Representative Words
  31. 31. Topic Analysis + User Stance + Tweet Sentiment (2018) [part 2] Topic Stance Sentiment Representative Words
  32. 32. Bot analysis Robots will take our jobs!
  33. 33. The higher the bot score, the more likely to have Pro-Leave stance Brambilla, Calisir. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case.
  34. 34. Concluding.. Don’t confuse evidence with a conclusion
  35. 35. Conclusions and Future Work PRESENT • A cool use case! Some surpri • A general method and implementation, configurable • Full paper https://arxiv.org/abs/1901.00740 FUTURE • Continuous collection of data • Analyze evolution of topics (topic drifting) • Apply Graph-based models • Deepen the social science interpretation part • Resources published (open data + code) Brambilla, Calisir. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case.
  36. 36. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case Thanks! Questions? @marcobrambi Data Science Lab Politecnico di Milano, Italy marco.brambilla@polimi.it Politecnico di Milano, Italy http://datascience.deib.polimi.it/ @datascience_mi

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