Twittering Public Sentiments: a Predictive Analysis of Pre-Poll Twitter Popularity of Prime Ministerial Candidates for the Indian Elections 2014

Twitter is a useful tool for predicting election outcomes, effectively complementing traditional opinion polling. This study undertakes a volume, sentiment and engagement analysis for predicting the popularity of Prime Ministerial candidates on Twitter as a run-up to the Indian Elections 2014. The results from a survey of 2,37,639 pre-poll tweets finds tweet volume as a significant predictor of candidate vote share, and volume and sentiments as predictors for candidate engagement levels. Higher engagement rates evolve from the horizontality of conversations about the candidate, therefore indicating a high degree of interactivity, but do not translate into a higher vote share

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

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.Comment: Accepted at EMNLP 2017 (long paper