Abstract
Predicting election results is a challenging task for big data analytics. Simple approaches count the number of tweets mentioning candidates or parties to do the prediction. In fact, many other factors may cause the candidates to win or lose in an election, such as their political opinions, social issues, and scandals. In this paper, we mine rules of event sequences from social media to predict election results. An example rule for a candidate can be as follows: “(big event, positive) → (small event, negative) → (big event, positive)” implies a victory to this candidate. We detect events and decide event types to generate event sequences and then apply the rule-based classifier to build the prediction model. A series of experiments are performed to evaluate our approaches and the experiment results reveal that the accuracy of our approaches on predicting election results is over 80 % in most of the cases.
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Tung, KC., Wang, E.T., Chen, A.L.P. (2016). Mining Event Sequences from Social Media for Election Prediction. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2016. Lecture Notes in Computer Science(), vol 9728. Springer, Cham. https://doi.org/10.1007/978-3-319-41561-1_20
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DOI: https://doi.org/10.1007/978-3-319-41561-1_20
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