Predicting Poll Trends Using Twitter and Multivariate Time-Series Classification

Social media outlets, such as Twitter, provide invaluable information for understanding the social and political climate surrounding particular issues. Millions of people who vary in age, social class, and political beliefs come together in conversation. However, this information poses challenges to making inferences from these tweets. Using the tweets from the 2016 U.S. Presidential campaign, one main research question is addressed in this work. That is, can accurate predictions be made detecting changes in a political candidate’s poll score trends utilizing tweets created during their campaign? The novelty of this work is that we formulate the problem as a multivariate time-series classification problem, which fits the temporal nature of tweets, rather than as a traditional attribute-based classification. Features that represent various aspects of support for (or against) a candidate are tracked on an hour-by-hour basis. Together these form multivariate time-series. One commonly used approach to this problem is based on the majority voting scheme. This method assumes the univariate time-series from different features have equal importance. To alleviate this issue a weighted shapelet transformation model is proposed. Extensive experiments on over 12 million tweets between November 2015 and January 2016 related to the four primary candidates (Bernie Sanders, Hillary Clinton, Donald Trump and Ted Cruz) indicate that the multivariate time-series approach outperforms traditional attribute-based approaches.