The 2020 US Presidential election was historic in that it featured the first woman of color, Kamala Harris, on a major-party ticket. Although Harris identifies as Black, her racial identity was widely scrutinized throughout the election, due to her mixed-race ancestry. Moreover, media coverage of Harris's racial identity appeared to vary based on that news outlet's political leaning and sometimes had prejudicial undertones. The current research investigated racial categorization of Harris and the role that political orientation and anti-Black prejudice might play in shaping these categorizations. Studies 1 and 2 tested the possibility that conservatives and liberals might mentally represent Harris differently, which we hypothesized would lead the two groups to differ in how they categorized her race. Contrary to our prediction, conservatives, and liberals mentally represented Harris similarly. Also surprising were the explicit racial categorization data. Conservatives labeled Harris as White more than liberals, who tended to categorize Harris as multiracial. This pattern was explained by anti-Black prejudice. Study 3 examined a potential political motivation that might explain this finding. We found that conservatives, more than liberals, judge having a non-White candidate on a Democratic ballot as an asset, which may lead conservatives to deny non-White candidates these identities.
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.
- Award ID(s):
- 1704527
- Publication Date:
- NSF-PAR ID:
- 10244753
- Journal Name:
- Proceedings of the Fourteenth International AAAI Conference on Web and Social Media (ICWSM 2020)
- Sponsoring Org:
- National Science Foundation
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