Understanding election candidate approval ratings using social media data

The last few years has seen an exponential increase in the amount of social media data generated daily. Thus, researchers have started exploring the use of social media data in building recommendation systems, prediction models, improving disaster management, discovery trending topics etc. An interesting application of social media is for the prediction of election results. The recently conducted 2012 US Presidential election was the "most tweeted" election in history and provides a rich source of social media posts. Previous work on predicting election outcomes from social media has been largely been based on sentiment about candidates, total volumes of tweets expressing electoral polarity and the like. In this paper we use a collection of tweets to predict the daily approval ratings of the two US presidential candidates and also identify topics that were causal to the approval ratings.