论文标题
在Facebook上分析政治运动的弱监督学习
Weakly Supervised Learning for Analyzing Political Campaigns on Facebook
论文作者
论文摘要
社交媒体平台目前是政治消息传递的主要渠道，使政客能够针对特定的人口统计数据并根据他们的反应进行适应。但是，使这种通信透明是具有挑战性的，因为消息传递与预定的受众紧密相结合，并且经常受到有兴趣推进特定政策的多个利益相关者的回应。本文我们的目标是迈出第一步，以理解这些高度分散的设置。我们提出了一种弱监督的方法，以确定Facebook上政治广告的立场和问题，并分析政治运动如何按照地点，性别或年龄来使用某种人口统计目标。此外，我们分析了选举民意测验的政治广告的时间动态。
Social media platforms are currently the main channel for political messaging, allowing politicians to target specific demographics and adapt based on their reactions. However, making this communication transparent is challenging, as the messaging is tightly coupled with its intended audience and often echoed by multiple stakeholders interested in advancing specific policies. Our goal in this paper is to take a first step towards understanding these highly decentralized settings. We propose a weakly supervised approach to identify the stance and issue of political ads on Facebook and analyze how political campaigns use some kind of demographic targeting by location, gender, or age. Furthermore, we analyze the temporal dynamics of the political ads on election polls.
