Title: Social Media Would Not Lie: Prediction of the 2016 Taiwan Election via Online Heterogeneous Data,
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 social media data collected from various online
platforms to predict Taiwan's 2016 presidential 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.