Mining Twitter big data to predict 2013 Pakistan election winner

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Abstract
Twitter is a well-known micro-blogging website which allows millions of users to interact over different types of communities, topics, and tweeting trends. The big data being generated on Twitter daily, and its significant impact on social networking, has motivated the application of data mining (analysis) to extract useful information from tweets. In this paper, we analyze the impact of tweets in predicting the winner of the recent 2013 election held in Pakistan. We identify relevant Twitter users, pre-process their tweets, and construct predictive models for three representative political parties which were significantly tweeted, i.e., Pakistan Tehreek-e-Insaaf (PTI), Pakistan Muslim League Nawaz (PMLN), and Muttahida Qaumi Movement (MQM). The predictions for last four days before the elections showed that PTI will emerge as the election winner, which was actually won by PMLN. However, considering that PTI obtained landslide victory in one province and bagged several important seats across the country, we conclude that Twitter can have some type of a positive influence on the election result, although it cannot be considered representative of the overall voting population.

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