Buzzer Detection and Sentiment Analysis for Predicting Presidential Election Results in a Twitter Nation

In this paper, we present our approach for predicting the results of Indonesian Presidential Election using Twitter as our main resource. We explore the possibility of easy-togather Twitter data to be utilized as a survey supporting tool to understand public opinion. First, we collected Twitter data during the campaign period. Second, we performed automatic buzzer detection on our Twitter data to remove those tweets generated by computer bots, paid users, and fanatic users that usually become noise in our data. Third, we performed a fine-grained political sentiment analysis to partition each tweet into several sub-tweets and subsequently assigned each sub-tweet with one of the candidates and its sentiment polarity. Finally, to predict the election results, we leveraged the number of positive sub-tweets for each candidate. Our experiment shows that the mean absolute error (MAE) of our Twitter-based prediction is 0.61%, which is surprisingly better than the prediction results published by several independent survey institutions (offline polls). Our study suggests that Twitter can serve as an important resource for any political activity, specifically for predicting the final outcomes of the election itself.