“i have a feeling trump will win………………”: Forecasting Winners and Losers from User Predictions on Twitter
Table 8: Performance of Veridicality, Sentiment baseline, and Frequency baseline on all event categories (%).
['[BOLD] Event', 'Veridicality P', 'Veridicality R', 'Veridicality F1', 'Sentiment P', 'Sentiment R', 'Sentiment F1', 'Frequency P', 'Frequency R', 'Frequency F1', '#predictions']
The veridicality-based approach outperforms sentiment and volume-based approaches on 9 of the 10 events considered. For the Tennis Grand Slam, the three approaches perform poorly. The difference in performance for the veridicality approach is quite lower for the Tennis events than for the other events. It is well known however that winners of tennis tournaments are very hard to predict. The performance of the players in the last minutes of the match are decisive, and even professionals have a difficult time predicting tennis winners.