论文标题
旨在测量美国中期选举中针对候选人的对抗性Twitter互动
Towards Measuring Adversarial Twitter Interactions against Candidates in the US Midterm Elections
论文作者
论文摘要
在Twitter等社交媒体上与政客的对抗性互动对社会有重大影响。特别是他们在网上破坏了实质性的政治讨论，并可能阻止人们寻求公职。在这项研究中，我们在2018年美国大选期间衡量了针对美国众议院候选人的对抗性互动。我们收集了一个新的数据集，其中包括170万条涉及候选人的推文，这是关注政治话语的最大语料库之一。然后，我们开发了一种新技术，用于检测针对任何特定候选人的有毒内容的推文。这使我们能够更准确地量化针对政治候选人的对抗性互动。此外，我们引入了一种算法来诱导特定的对抗术语，以捕获以前技术可能不认为有毒的更细微的对抗相互作用。最后，我们使用这些技术来概述选举中看到的对抗性互动的广度，包括进攻性的名称，暴力威胁，发布抹黑的信息，对身份的攻击和对抗消息的重复。
Adversarial interactions against politicians on social media such as Twitter have significant impact on society. In particular they disrupt substantive political discussions online, and may discourage people from seeking public office. In this study, we measure the adversarial interactions against candidates for the US House of Representatives during the run-up to the 2018 US general election. We gather a new dataset consisting of 1.7 million tweets involving candidates, one of the largest corpora focusing on political discourse. We then develop a new technique for detecting tweets with toxic content that are directed at any specific candidate.Such technique allows us to more accurately quantify adversarial interactions towards political candidates. Further, we introduce an algorithm to induce candidate-specific adversarial terms to capture more nuanced adversarial interactions that previous techniques may not consider toxic. Finally, we use these techniques to outline the breadth of adversarial interactions seen in the election, including offensive name-calling, threats of violence, posting discrediting information, attacks on identity, and adversarial message repetition.
