“i have a feeling trump will win..................”: Forecasting Winners and Losers from User Predictions on Twitter

Sandesh Swamy, Alan Ritter, Marie-Catherine de Marneffe


Abstract
Social media users often make explicit predictions about upcoming events. Such statements vary in the degree of certainty the author expresses toward the outcome: “Leonardo DiCaprio will win Best Actor” vs. “Leonardo DiCaprio may win” or “No way Leonardo wins!”. Can popular beliefs on social media predict who will win? To answer this question, we build a corpus of tweets annotated for veridicality on which we train a log-linear classifier that detects positive veridicality with high precision. We then forecast uncertain outcomes using the wisdom of crowds, by aggregating users’ explicit predictions. Our method for forecasting winners is fully automated, relying only on a set of contenders as input. It requires no training data of past outcomes and outperforms sentiment and tweet volume baselines on a broad range of contest prediction tasks. We further demonstrate how our approach can be used to measure the reliability of individual accounts’ predictions and retrospectively identify surprise outcomes.
Anthology ID:
D17-1166
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1583–1592
Language:
URL:
https://www.aclweb.org/anthology/D17-1166
DOI:
10.18653/v1/D17-1166
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1166.pdf
Video:
 https://vimeo.com/238236876