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

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. 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. 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. 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. 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. 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. 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. 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. Analysis (1)

10. Analysis (2)

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. 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