论文标题
Wikipedia：挑战者最好的朋友？利用寻求信息的行为模式来预测美国国会选举
Wikipedia: A Challenger's Best Friend? Utilising Information-seeking Behaviour Patterns to Predict US Congressional Elections
论文作者
论文摘要
长期以来，选举预测一直是政治学文学中的常绿。传统上，这样的努力包括投票汇总，经济指标，党派隶属关系和竞选效应，以预测总投票结果。随着社会科学中在线生成数据的次要使用的越来越多，研究人员开始从广泛使用的基于Web的平台（例如Facebook，Twitter，Google趋势和Wikipedia）咨询元数据，以校准预测模型。基于Web的平台为选民提供了获取与广告系列相关的详细信息的手段，并为研究人员研究了围绕它们的广告系列和公众情绪的普及。但是，过去的贡献经常忽略了传统选举变量与寻求信息的行为模式之间的相互作用。在这项工作中，我们旨在通过考虑信息检索在现任和挑战者活动之间的区别，以及感知到的候选人生存能力和媒体报道对Wikipedia PageView的预测能力的影响，旨在统一传统和新颖的方法。为了检验我们的假设，我们在2016年至2018年之间使用美国国会（参议院和众议院）选举的选举数据。我们证明，Wikipedia数据作为寻求信息的行为模式的代理，对于预测媒体中涵盖的良好挑战者的成功特别有用。通常，我们的发现强调了混合数据方法在计算社会科学中预测分析的重要性。
Election prediction has long been an evergreen in political science literature. Traditionally, such efforts included polling aggregates, economic indicators, partisan affiliation, and campaign effects to predict aggregate voting outcomes. With increasing secondary usage of online-generated data in social science, researchers have begun to consult metadata from widely used web-based platforms such as Facebook, Twitter, Google Trends and Wikipedia to calibrate forecasting models. Web-based platforms offer the means for voters to retrieve detailed campaign-related information, and for researchers to study the popularity of campaigns and public sentiment surrounding them. However, past contributions have often overlooked the interaction between conventional election variables and information-seeking behaviour patterns. In this work, we aim to unify traditional and novel methodology by considering how information retrieval differs between incumbent and challenger campaigns, as well as the effect of perceived candidate viability and media coverage on Wikipedia pageviews predictive ability. In order to test our hypotheses, we use election data from United States Congressional (Senate and House) elections between 2016 and 2018. We demonstrate that Wikipedia data, as a proxy for information-seeking behaviour patterns, is particularly useful for predicting the success of well-funded challengers who are relatively less covered in the media. In general, our findings underline the importance of a mixed-data approach to predictive analytics in computational social science.
