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Twitter as a new form of social media potentially contains useful informationthat opens new opportunities for content analysis on tweets. This paperexamines the predictive power of Twitter regarding the US presidential electionof 2012. For this study, we analyzed 32 million tweets regarding the USpresidential election by employing a combination of machine learningtechniques. We devised an advanced classifier for sentiment analysis in orderto increase the accuracy of Twitter content analysis. We carried out ouranalysis by comparing Twitter results with traditional opinion polls. Inaddition, we used the Latent Dirichlet Allocation model to extract theunderlying topical structure from the selected tweets. Our results show that wecan determine the popularity of candidates by running sentiment analysis. Wecan also uncover candidates popularities in the US states by running thesentiment analysis algorithm on geo-tagged tweets. To the best of ourknowledge, no previous work in the field has presented a systematic analysis ofa considerable number of tweets employing a combination of analysis techniquesby which we conducted this study. Thus, our results aptly suggest that Twitteras a well-known social medium is a valid source in predicting future eventssuch as elections. This implies that understanding public opinions and trendsvia social media in turn allows us to propose a cost- and time-effective waynot only for spreading and sharing information, but also for predicting futureevents.

The Predictive Power of Social Media: On the Predictability of U.S. Presidential Elections using Twitter