Numerous politicians use social media platforms, particularly X, to engage with their constituents. This interaction allows constituents to pose questions and offer feedback but also exposes politicians to a barrage of hostile responses, especially given the anonymity afforded by social media. They are typically targeted in relation to their governmental role, but the comments also tend to attack their personal identity. This can discredit politicians and reduce public trust in the government. It can also incite anger and disrespect, leading to offline harm and violence. While numerous models exist for detecting hostility in general, they lack the specificity required for political contexts. Furthermore, addressing hostility towards politicians demands tailored approaches due to the distinct language and issues inherent to each country (e.g., Brexit for the UK). To bridge this gap, we construct a dataset of 3,320 English tweets spanning a two-year period manually annotated for hostility towards UK MPs. Our dataset also captures the targeted identity characteristics (race, gender, religion, none) in hostile tweets. We perform linguistic and topical analyses to delve into the unique content of the UK political data. Finally, we evaluate the performance of pre-trained language models and large language models on binary hostility detection and multi-class targeted identity type classification tasks. Our study offers valuable data and insights for future research on the prevalence and nature of politics-related hostility specific to the UK.

翻译：众多政治家利用社交媒体平台，特别是X平台，与选民进行互动。这种互动使选民能够提出问题并提供反馈，但也使政治家暴露在大量敌意回应之下，尤其是考虑到社交媒体提供的匿名性。他们通常因其政府角色而成为攻击目标，但评论也往往针对其个人身份进行攻击。这可能损害政治家的声誉，并降低公众对政府的信任。它还可能煽动愤怒和不尊重，导致线下伤害和暴力。虽然存在许多用于检测一般敌意的模型，但它们缺乏政治背景所需的特异性。此外，由于每个国家独特的语言和议题（例如英国的脱欧问题），处理针对政治家的敌意需要定制化的方法。为填补这一空白，我们构建了一个包含3,320条英文推文的数据集，这些推文跨越两年时间，并经过人工标注，用于识别针对英国议员的敌意。我们的数据集还捕获了敌意推文中针对的身份特征（种族、性别、宗教、无）。我们进行了语言和主题分析，以深入探究英国政治数据的独特内容。最后，我们评估了预训练语言模型和大型语言模型在二元敌意检测和多类目标身份类型分类任务上的性能。我们的研究为未来针对英国特有的政治相关敌意的普遍性和性质的研究提供了宝贵的数据和见解。