Projects per year
Abstract
Social media is now used as a forecasting tool by a variety of firms and agencies.
But how useful are such data in forecasting outcomes? Can social media add any information to that produced by a prediction/betting market? We source 13.8m posts from Twitter, and combine them with contemporaneous Betfair betting prices, to forecast the outcomes of English Premier League soccer matches as they unfold. Using a micro-blogging dictionary to analyse the content of Tweets, we find that the aggregate tone of Tweets contains significant information not in betting prices, particularly in the immediate aftermath of goals and red cards.
But how useful are such data in forecasting outcomes? Can social media add any information to that produced by a prediction/betting market? We source 13.8m posts from Twitter, and combine them with contemporaneous Betfair betting prices, to forecast the outcomes of English Premier League soccer matches as they unfold. Using a micro-blogging dictionary to analyse the content of Tweets, we find that the aggregate tone of Tweets contains significant information not in betting prices, particularly in the immediate aftermath of goals and red cards.
Original language | English |
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Pages (from-to) | 1748-1763 |
Number of pages | 16 |
Journal | Economic Inquiry |
Volume | 56 |
Issue number | 3 |
Early online date | 12 Oct 2017 |
DOIs | |
Publication status | Published - Jul 2018 |
Keywords
- forecasting
- social media
- prediction markets
- wisdom of crowds
- soccer
Profiles
-
Alasdair Brown
- School of Economics - Associate Professor in Economics
- Applied Econometrics And Finance - Member
Person: Research Group Member, Academic, Teaching & Research
Projects
- 1 Finished