Twitter as a new form of social media potentially contains useful information that opens new opportunities for content analysis on tweets. This paper examines the predictive power of Twitter regarding the US presidential election of 2012. For this study, we analyzed 32 million tweets regarding the US presidential election by employing a combination of machine learning techniques. We devised an advanced classifier for sentiment analysis in order to increase the accuracy of Twitter content analysis. We carried out our analysis by comparing Twitter results with traditional opinion polls. In addition, we used the Latent Dirichlet Allocation model to extract the underlying topical structure from the selected tweets. Our results show that we can determine the popularity of candidates by running sentiment analysis. We can also uncover candidates popularities in the US states by running the sentiment analysis algorithm on geo-tagged tweets. To the best of our knowledge, no previous work in the field has presented a systematic analysis of a considerable number of tweets employing a combination of analysis techniques by which we conducted this study. Thus, our results aptly suggest that Twitter as a well-known social medium is a valid source in predicting future events such as elections. This implies that understanding public opinions and trends via social media in turn allows us to propose a cost- and time-effective way not only for spreading and sharing information, but also for predicting future events.
To what extend can one use Twitter in opinion polls for political elections? Merely counting Twitter messages mentioning political party names is no guarantee for obtaining good election predictions. By improving the quality of the document collection and by performing sentiment analysis, predictions based on entity counts in tweets can be considerably improved, and become nearly as good as traditionally obtained opinion polls.  In recent years several researchers have reported that the volume of Google Trends and Twitter chat over time can be used to predict several kinds of social and consumer metrics. From the success of new movies before their release to the marketability of consumer goods to the prediction of voting results in the recent 2009 German elections, Google Trends and Twitter message volume have been treated as indispensable tools for not only recording current social trends, but even for predicting the future. This is surprising, given the significant differences in the demographics of voters and people who use social networks and Web tools. But is there some underline logic behind these predictions or are they simply a matter of luck? With this work we wanted to test their predictive power against the US elections. One could argue that, following the previous research literature, and given the high utilization that the Web and the social networks have in the US, Google Trends and Twitter volume may be able to predict the outcomes of the US Congressional elections. In this paper we report that Google Trends was, actually, not a good predictor of both the 2008 and 2010 elections, and we offer some explanation on why this may be the case. On a forthcoming paper we report on our analysis on Twitter.