Institute for the Study of Global Issues, Graduate School of Social Sciences, Hitotsubashi University, Kunitachi, Tokyo, Japan.
PLoS One. 2018 Nov 28;13(11):e0208190. doi: 10.1371/journal.pone.0208190. eCollection 2018.
This paper investigates the relationship between candidates' online popularity and election results, as a step towards creating a model to forecast the results of Taiwanese elections even in the absence of reliable opinion polls on a district-by-district level. 253 of 354 legislative candidates of single-member districts in Taiwan's 2016 general election had active public Facebook pages during the election period. Hypothesizing that the relative popularity of candidates' Facebook posts will be positively related to their election results, I calculated each candidate's Like Ratio (i.e. proportions of all likes on Facebook posts obtained by candidates in their district). In order to have a measure of online interest without the influence of subjective positivity, I similarly calculated the proportion of daily average page views for each candidate's Wikipedia page. I ran a regression analysis, incorporating data on results of previous elections and available opinion poll data. I found the models could describe the result of the election well and reject the null hypothesis. My models successfully predicted 80% of winners in single-member districts and were effective in districts without local opinion polls with a predictive power approaching that of traditional opinion polls. The models also showed good accuracy when run on data for the 2014 Taiwanese municipal mayors election.
本文探讨了候选人的网络人气与选举结果之间的关系，旨在建立一个模型，以便即使在缺乏可靠的分区民意调查的情况下，也能预测台湾选举的结果。在 2016 年台湾地区领导人选举中，253 位选区制立委候选人在选举期间都有活跃的公共 Facebook 页面。本文假设候选人的 Facebook 帖子的相对人气将与其选举结果呈正相关，因此计算了每位候选人的“赞比”（即候选人在其选区获得的所有 Facebook 帖子赞数的比例）。为了在不受主观积极性影响的情况下衡量在线兴趣，我还计算了候选人维基百科页面的每日平均页面浏览量比例。我进行了回归分析，纳入了以往选举结果和可用民意调查数据。结果表明，这些模型能够很好地描述选举结果，并拒绝了零假设。我的模型成功预测了 80%的单一选区的获胜者，在没有地方民意调查的选区也具有预测能力，其预测能力接近传统民意调查。该模型在 2014 年台湾市长选举的数据上也表现出了良好的准确性。
