Predicting political preference of Twitter users

We study the problem of predicting the political preference of users on the Twitter network, showing that the political preference of users can be predicted from their Twitter behavior towards political parties. We show this by building prediction models based on a variety of contextual and behavioral features, training the models by resorting to a distant supervision approach and considering party candidates to have a predefined preference towards their respective parties. A language model for each party is learned from the content of the tweets by the party candidates, and the preference of a user is assessed based on the alignment of user tweets with the language models of the parties. We evaluate our work in the context of two real elections: 2012 Albertan and 2013 Pakistani general elections. In both cases, we show that our model outperforms, in terms of the F-measure, sentiment and text classification approaches and is at par with the human annotators. We further use our model to analyze the preference changes over the course of the election campaign and report results that would be difficult to attain by human annotators.

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