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The Potential and Perils of Election Prediction Using Social Media Sources

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Presentation given at Connected Life 2016, Oxford Internet Institute, University of Oxford, June 2016

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  1. 1. The Potential and Perils of Election Prediction Using Social Media Sources Federico Nanni and Josh Cowls University of Mannheim/Comparative Media Studies, MIT
  2. 2. Reasons to be cheerful + Social media data is (often) cheap + Phone response rates are in decline + More granularity available? Cost Utility Traditional inferential model Social media model
  3. 3. Reasons to be doubtful - Myriad reliability issues... – Difficult to establish the meaning of latent messages – Platform specific behaviours (e.g. hashtags, likes) are not always understood – Political discourse often laced with e.g. sarcasm - The ethics of collecting and using social media data
  4. 4. Results to date have been mixed... • A meta-analysis found little evidence that using Twitter to predict elections is better than chance in the aggregate (Gayo-Avello, 2013) • Nonetheless, social media can provide an ‘early warning system’ for a candidate’s momentum (Jensen and Anstead, 2013) • Big problem: what’s in a name?
  5. 5. Our approach: intention over attention • Most models count references to candidates’ or parties’ names – measuring attention • Other models use sentiment analysis, seeking to ascertain emotion responses to candidates • We built an intention model, collecting instances of vote declarations for specific candidates
  6. 6. Case study • Context: Labour and the Lib Dems required new leaders in 2015 (after a polling fail!) • Leadership elections conducted in summer 2015 – Lib Dems: two candidates (Tim Farron, Norman Lamb) – Labour: four candidates (Jeremy Corbyn, Andy Burnham, Yvette Cooper, Liz Kendall)
  7. 7. Advantages of our case • Primary candidates’ names easier to isolate than ambiguous party names (“Labour”, “Liberal”) • Party elections are a minority sport – better signal to noise ratio? • Start and end dates clear; postal vote system ensured greater period of decision-making
  8. 8. Method  Wrote Python scripts to collect tweets which:  Mentioned the name of a candidate  Included a specific declaration to vote (“I’ll vote for...”, “I’m voting for” etc)  Cleaned data  Removed non-declarations (“I’m not voting for...”)  Ascertained preferred candidate in ambiguous cases  Final dataset: 1361 valid declarations for Lib Dem race and 17617 for Labour
  9. 9. Analysis (1)
  10. 10. Analysis (2)
  11. 11. Key successes • ‘Intention’ model beat out ‘Attention’ model in 5 out of 6 races, and in both races overall • Lib Dem prediction accuracy close to traditional margin of error (MOE = 3.5) • Caught Corbyn’s success to a high degree of accuracy (MOE = 2)
  12. 12. Reflections and future work • Tough to generalise successes – specific cases, particular platform. (How) would this work for: – Multi-state process (e.g. US primaries)? – General elections? • Despite ongoing challenges, social media will surely play a key role in the future of accurate election prediction

Description

Presentation given at Connected Life 2016, Oxford Internet Institute, University of Oxford, June 2016

Transcription

  1. 1. The Potential and Perils of Election Prediction Using Social Media Sources Federico Nanni and Josh Cowls University of Mannheim/Comparative Media Studies, MIT
  2. 2. Reasons to be cheerful + Social media data is (often) cheap + Phone response rates are in decline + More granularity available? Cost Utility Traditional inferential model Social media model
  3. 3. Reasons to be doubtful - Myriad reliability issues... – Difficult to establish the meaning of latent messages – Platform specific behaviours (e.g. hashtags, likes) are not always understood – Political discourse often laced with e.g. sarcasm - The ethics of collecting and using social media data
  4. 4. Results to date have been mixed... • A meta-analysis found little evidence that using Twitter to predict elections is better than chance in the aggregate (Gayo-Avello, 2013) • Nonetheless, social media can provide an ‘early warning system’ for a candidate’s momentum (Jensen and Anstead, 2013) • Big problem: what’s in a name?
  5. 5. Our approach: intention over attention • Most models count references to candidates’ or parties’ names – measuring attention • Other models use sentiment analysis, seeking to ascertain emotion responses to candidates • We built an intention model, collecting instances of vote declarations for specific candidates
  6. 6. Case study • Context: Labour and the Lib Dems required new leaders in 2015 (after a polling fail!) • Leadership elections conducted in summer 2015 – Lib Dems: two candidates (Tim Farron, Norman Lamb) – Labour: four candidates (Jeremy Corbyn, Andy Burnham, Yvette Cooper, Liz Kendall)
  7. 7. Advantages of our case • Primary candidates’ names easier to isolate than ambiguous party names (“Labour”, “Liberal”) • Party elections are a minority sport – better signal to noise ratio? • Start and end dates clear; postal vote system ensured greater period of decision-making
  8. 8. Method  Wrote Python scripts to collect tweets which:  Mentioned the name of a candidate  Included a specific declaration to vote (“I’ll vote for...”, “I’m voting for” etc)  Cleaned data  Removed non-declarations (“I’m not voting for...”)  Ascertained preferred candidate in ambiguous cases  Final dataset: 1361 valid declarations for Lib Dem race and 17617 for Labour
  9. 9. Analysis (1)
  10. 10. Analysis (2)
  11. 11. Key successes • ‘Intention’ model beat out ‘Attention’ model in 5 out of 6 races, and in both races overall • Lib Dem prediction accuracy close to traditional margin of error (MOE = 3.5) • Caught Corbyn’s success to a high degree of accuracy (MOE = 2)
  12. 12. Reflections and future work • Tough to generalise successes – specific cases, particular platform. (How) would this work for: – Multi-state process (e.g. US primaries)? – General elections? • Despite ongoing challenges, social media will surely play a key role in the future of accurate election prediction

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