Abstract
The prevalence of online media has attracted researchers from various domains to explore human behavior and make interesting predictions. In this research, we leverage heterogeneous data collected from various online platforms to predict Taiwan’s 2016 general election. In contrast to most existing research, we take a “signal” view of heterogeneous information and adopt the Kalman filter to fuse multiple signals into daily vote predictions for the candidates. We also consider events that influenced the election in a quantitative manner based on the so-called event study model that originated in the field of financial research. We obtained the following interesting findings. First, public opinions in online media dominate traditional polls in Taiwan election prediction in terms of both predictive power and timeliness. But offline polls can still function on alleviating the sample bias of online opinions. Second, although online signals converge as election day approaches, the simple Facebook “Like” is consistently the strongest indicator of the election result. Third, most influential events have a strong connection to cross-strait relations, and the Chou Tzu-yu flag incident followed by the apology video one day before the election increased the vote share of Tsai Ing-Wen by 3.66%. This research justifies the predictive power of online media in politics and the advantages of information fusion. The combined use of the Kalman filter and the event study method contributes to the data-driven political analytics paradigm for both prediction and attribution purposes.
Highlights
Recent years have witnessed the rapid development of social media and their innovative applications in many fields [1]
Public moods extracted from tweets can predict changes in stock markets [3, 4], and a real-time earthquake reporting system was developed by analyzing only tweets [5]
Our study suggests that the Kalman filter with the event detection model could be packaged as a fundamental kit for political vote analytics
Summary
Recent years have witnessed the rapid development of social media and their innovative applications in many fields [1]. Xie et al EPJ Data Science (2018) 7:32 political ideas on social media and even debate on social media before and during the campaign These behaviors can attract online discussion from massive numbers of netizens and, compared with traditional polls, are an easier way to gather wide-ranging public opinions about the candidates. This research leverages time series data collected from various mainstream online platforms (i.e., Facebook, Twitter and Google) and visitation traffic to candidates’ campaign pages. These heterogeneous signals represent public opinions and are fed into a Kalman filter [22] to estimate the vote shares of each candidate dynamically. Where wi is the population proportion of age group i, which could be obtained from the Ministry of the Interior of Taiwan.b zic,k is the most recent poll result of age group i for candidate c on day k
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