Towards Measuring Adversarial Twitter Interactions against Candidates in the US Midterm Elections

• URL: http://arxiv.org/abs/2005.04411v1
• Date: Sat, 9 May 2020 10:00:41 GMT
• Title: Towards Measuring Adversarial Twitter Interactions against Candidates in the US Midterm Elections
• Authors: Yiqing Hua, Thomas Ristenpart, Mor Naaman
• Abstract summary: We measure the adversarial interactions against candidates for the US House of Representatives during the run-up to the 2018 US general election. We develop a new technique for detecting tweets with toxic content that are directed at any specific candidate. 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.
• Score: 25.374045377135307
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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