Original
Measuring Partisanship and Representation in Online Congressional Communications
Abstract
Social media and the internet have created new ways for representatives to communicate. How have members of Congress responded to these opportunities? We introduce a multi-platform dataset of congressional communications extending back to the onset of the social media era. Using computational language processing, we classify approximately 4.7 million tweets, 2.4 million Facebook posts, and 184,000 email newsletters authored by members of Congress between 2009 and 2022 based on intended purpose, and scale the partisanship of each message along a continuous left–right dimension. After validation, we demonstrate how our data can be used to study partisanship and representation in the contemporary Congress. Importantly, our data show congressional rhetoric has become more partisan and negative as social media usage has increased. We identify one potential mechanism contributing to this trend: partisanship and negativity receive inflated levels of positive engagement on social media relative to other forms of messaging like credit claiming or constituency service.
中文
测量在线国会通讯中的党派性与代表性
摘要
社交媒体和互联网为国会议员创造了新的沟通方式。国会议员如何回应这些机会？我们构建了一个可追溯至社交媒体时代初期的多平台国会通讯数据集。使用计算语言处理，我们根据预期目的，对2009年至2022年间国会议员撰写的约470万条推文、240万条脸书帖子和18.4万份电子邮件通讯进行分类，并将每条信息的党派性沿连续的左—右维度进行测量。经过验证后，我们展示了如何使用我们的数据来研究当代国会中的党派性与代表性。重要的是，我们的数据显示，随着社交媒体使用增加，国会修辞变得更具党派性和负面性。我们识别出一个可能促成这一趋势的机制：相对于其他信息形式（如邀功或选区服务），党派性和负面性在社交媒体上获得不成比例的高水平正向互动。
关键词
社交媒体、国会通讯、党派性、政治代表性、美国国会、计算文本分析、政治传播