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Title: Towards Measuring Adversarial TwitterInteractions against Candidates in the US Midterm Elections
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 con-tent 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.  more » « less
Award ID(s):
1704527
PAR ID:
10244753
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of the Fourteenth International AAAI Conference on Web and Social Media (ICWSM 2020)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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