Social Media Would Not Lie: Prediction of the 2016 Taiwan Election via Online Heterogeneous Data

TL;DR

This study demonstrates that online social media data, especially Facebook 'Likes' and key political events, can effectively predict election outcomes, surpassing traditional polls in timeliness and accuracy.

Contribution

It introduces a novel approach combining Kalman filtering and event study models to fuse heterogeneous online signals for election prediction and attribution.

Findings

Online media opinions outperform traditional polls in prediction accuracy.

Facebook 'Like' is the strongest online indicator of election results.

Major political events, like the Chou Tzu-yu incident, significantly influenced vote shares.

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.