@inproceedings{swamy-etal-2017-feeling,
title = "{``}i have a feeling trump will win..................{''}: Forecasting Winners and Losers from User Predictions on {T}witter",
author = "Swamy, Sandesh and
Ritter, Alan and
de Marneffe, Marie-Catherine",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1166",
doi = "10.18653/v1/D17-1166",
pages = "1583--1592",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T “i have a feeling trump will win..................”: Forecasting Winners and Losers from User Predictions on Twitter
%A Swamy, Sandesh
%A Ritter, Alan
%A de Marneffe, Marie-Catherine
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F swamy-etal-2017-feeling
%X 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.
%R 10.18653/v1/D17-1166
%U https://aclanthology.org/D17-1166
%U https://doi.org/10.18653/v1/D17-1166
%P 1583-1592
Markdown (Informal)
[“i have a feeling trump will win..................”: Forecasting Winners and Losers from User Predictions on Twitter](https://aclanthology.org/D17-1166) (Swamy et al., EMNLP 2017)
ACL