The past decade saw remarkable growth in the production of user-generated text data due to ever-increasing usage of social media. During the same time Twitter has become an indispensable communication tool for politicians. To explore this link, we examine what usage patterns reveal about users’ opinions on the issue of Brexit, these usage patterns consisting of tweet frequency and length, as well as the terms used and their length. We analyse 185,970 tweets from 576 twitter accounts, each account associated with a Member of the British Parliament (MP). We use regression analysis and sentiment analysis, namely Term Frequency–Inverse Document Frequency (TF–IDF), to investigate if there is a relationship between the features of text data and the characteristics of Twitter users. Whereas these methods have previously been applied to American two-party politics, the multiple parties of the British political landscape have led to previous studies using typological analysis (human classifiers) to identify tweets. We present a methodology that assigns a political value based on an MP’s voting record on a single issue (Brexit). We identify systematic yet subtle differences in the way the two sides of the debate use language, but also specific usage patterns that are common to both.

在过去十年中，由于社交媒体的使用不断增加，用户生成的文本数据的产生显着增长。与此同时，Twitter 已成为政治家不可或缺的沟通工具。为了探索此链接，我们研究了哪些使用模式揭示了用户对英国退欧问题的看法，这些使用模式包括推文频率和长度，以及使用的术语及其长度。我们分析了来自 576 个推特账户的 185,970 条推文，每个账户都与一位英国议会议员 (MP) 相关联。我们使用回归分析和情感分析，即词频-逆文档频率（TF-IDF）来调查文本数据的特征与推特用户特征之间是否存在关系。尽管这些方法之前已应用于美国两党政治，但英国政治格局的多党导致之前的研究使用类型学分析（人类分类器）来识别推文。我们提出了一种方法，该方法根据议员在单个问题（英国退欧）上的投票记录来分配政治价值。我们确定了辩论双方在使用语言的方式上的系统但细微的差异，以及双方共有的特定使用模式。