Leveraging candidate popularity on Twitter to predict election outcome

Authors:
Manish Gaurav;Amit Srivastava;Anoop Kumar;Scott Miller
Affiliations:
Raytheon BBN Technologies, Cambridge, MA;Raytheon BBN Technologies, Cambridge, MA;Raytheon BBN Technologies, Cambridge, MA;Raytheon BBN Technologies, Cambridge, MA
Venue:
Proceedings of the 7th Workshop on Social Network Mining and Analysis
Year:
2013

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Abstract

In recent years, Twitter has become one of the most important modes for social networking and disseminating content on a variety of topics. It has developed into a popular medium for political discourse and social organization during elections. There has been growing body of literature demonstrating the ability to predict the outcome of elections from Twitter data. This works aims to test the predictive power of Twitter in inferring the winning candidate and vote percentages of the candidates in an election. Our prediction is based on the number of times the name of a candidate is mentioned in tweets prior to elections. We develop new methods to augment the counts by counting not only the presence of candidate's official names but also their aliases and commonly appearing names. In addition, we devised a technique to include relevant and filter irrelevant tweets based on predefined set of keywords. Our approach is successful in predicting the winner of all three presidential elections held in Latin America during the months of February through April, 2013.