2011 Spanish General Election
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2011 Spanish General Election

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Case study: 2011 Spanish General Election (Twitter as a communication channel, as an opinion sensor, as a connection net, as a source of prediction)

Case study: 2011 Spanish General Election (Twitter as a communication channel, as an opinion sensor, as a connection net, as a source of prediction)

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2011 Spanish General Election 2011 Spanish General Election Presentation Transcript

  • 2011 Spanish General Elections2012, July 6 M. Luz Congosto / Pablo Aragón 1
  • SUMMARYTwitter on Election campaign Status of art on Electoral predictionCase study: 2011 Spanish General ElectionConclusions / Findings2012, July 6 M. Luz Congosto / Pablo Aragón 2
  • Twitter on Election campaign Comunication Opinion Sensor Data Mining Prediction2012, July 6 M. Luz Congosto / Pablo Aragón 3
  • Status of art on Electoral prediction (Tumasjan, 2010) 2009 German Elections. Method count mentions 1,65% MAE (Mean Absolute Error) (Jungherr, 2011) 2009 German Elections (Conover D. , 2010) 2010 US Elections (Gayo-Avello D. , 2011) 2008 US elections (Tjong, 2012) 2011 Dutch Elections (Skoric, 2012) 2011 Singapur 2011 (Bermingham et al., 2011) 2011 IIreland Elections (Panagiotis, 2011) 2010 US Elections2012, July 6 M. Luz Congosto / Pablo Aragón 4
  • Case study: 2011 Spanish General Election  Methodology  Twitter as a communication channel  Twitter as an opinion sensor  Twitter as a connection net  Twitter as a source of prediction 2012, July 6 M. Luz Congosto / Pablo Aragón 5
  • Case study: 2011 Spanish General Election Methodology – Dataset 1: Monitored tweets with mentions of national parties from 08/10/11 to 22/11/11 using Twitter streaming API from Carlos III University getting 2,973,110 tweets from 441,795 unique users – Dataset 2: Stored tweets with mentions of political parties represented in Parliament from 9-10-2011 to 24-11-2011 using a routing process for downloading some users timeline and Twitter streaming API from Fundació Barcelona Media getting 2,279,250 tweets from 442,014 unique users 2012, July 6 M. Luz Congosto / Pablo Aragón 6
  • Twitter as a communication channel Candidate vs. Party PSOE PP Rajoy Cayo Rubalcaba Lara UpyD Equo Treemap of followers before the campaing (Dataset-1)2012, July 6 M. Luz Congosto / Pablo Aragón 7
  • Twitter as a communication channel Activity of campaign accounts on Twitter Accumulated of tweets publish on campaign (Dataset-1)2012, July 6 M. Luz Congosto / Pablo Aragón 8
  • Twitter as a communication channel Getting new followers Accumulated of new followers on campaign(Dataset-1)2012, July 6 M. Luz Congosto / Pablo Aragón 9
  • Twitter as communication channel Correlation new followers / unique mentions Timeline (Dataset-1) Correlation by day (Dataset-1)2012, July 6 M. Luz Congosto / Pablo Aragón 10
  • Twitter as an opinion sensor Citizen Participation Tweets and users by day on campaign (Dataset-1)2012, July 6 M. Luz Congosto / Pablo Aragón 11
  • Twitter as an opinion sensor Emotionality (valence) Valence by day of campaign (Dataset-2)2012, July 6 M. Luz Congosto / Pablo Aragón 12
  • Twitter as an opinion sensor Emotionality (dominance) Dominance by day on campaign (Dataset-2)2012, July 6 M. Luz Congosto / Pablo Aragón 13
  • Twitter as an opinion sensor Spread links Europa Política. El País Press ABC El País Público El mundo Treemap of mentions of Web sites on campaign (Dataset-1) Inteactive image: http://barriblog.com/taller/javascript/protovis/sites_20N.html2012, July 6 M. Luz Congosto / Pablo Aragón 14
  • Twitter as a connection net User Communities Mapa of RTs between politicians on campaign (Dataset-2)2012, July 6 M. Luz Congosto / Pablo Aragón 15
  • Twitter as a source of prediction Mentions vs. Results Total mentions (name + @user + #hashtag) MAE=1,66% Mentions count on campaign (Dataset-1)2012, July 6 M. Luz Congosto / Pablo Aragón 16
  • Twitter as a source of prediction Political Polarity vs. Results Total users MAE: 5,00%Users with more than three polarity RTs or #hashtags on campaign (Dataset-1)2012, July 6 M. Luz Congosto / Pablo Aragón 17
  • Twitter as a source of prediction Political Polarity vs. ResultsMen(61,38%) MAE: 6,49% Women (38,62%) MAE: 3,88% Users with more than three polarity RTs or #hashtags on campaign (Dataset-1) 2012, July 6 M. Luz Congosto / Pablo Aragón 18
  • Conclusions / FindingsWith measurements based on the mentions count we havegot a good result, however:The results depend on many factors such as the social-culturalenvironment in the elections, the period of the sample, campaignevents, the collection of data on Twitter, the parties analyzedand calculation methodThe validation of this method of forecasting requiressystemization of steps and checking of other elections2012, July 6 M. Luz Congosto / Pablo Aragón 19
  • Conclusions / FindingsWith the measurements based on Political polarity we havegot worse results, we’ll have to bear this in mind toimprove the algorithms:•Demographics: Twitter users are young and highly educated.•Hidden opinion: Not all users show their political opinions•Over opinion: Some parties supporters are very actives•Entity vs. People: It’s difficult to distinguish an entity from aperson on Twitter•Anonymous vs. “Real Identity”: users with a real identity aremore likely to have a hidden opinion•Men vs. Women: There is a gender difference. Men are likelyto hide their opinion or to over opinion than women2012, July 6 M. Luz Congosto / Pablo Aragón 20