Predicting election outcomes is of considerable interest to candidates, political scientists, and the public at large. We propose the use of Web browsing history as a new indicator of candidate preference among the electorate, one that has potential to overcome a number of the drawbacks of election polls. However, there are a number of challenges that must be overcome to effectively use Web browsing for assessing candidate preference—including the lack of suitable ground truth data and the heterogeneity of user populations in time and space. We address these challenges, and show that the resulting methods can shed considerable light on the dynamics of voters’ candidate preferences in ways that are difficult
to achieve using polls.
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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.
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- Award ID(s):
- 1704527
- NSF-PAR ID:
- 10244753
- 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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Recent studies of clientelism predominantly focus on how elites use rewards to influence vote choices and turnout. This article shifts attention toward citizens and their choices beyond the ballot box. Under what conditions does clientelism influence citizens’ decisions to express political preferences publicly? When voters can obtain post-election benefits by declaring support for victorious candidates, their choices to display political paraphernalia on their homes or bodies may reflect more than just political preferences. We argue that various factors—such as the size of rewards and punishments, the competitiveness of the election, and whether multiple candidates employ clientelism—affect citizens’ propensity to declare support in response to clientelist inducements. Building on insights from fieldwork, formal analyses reveal how and why such factors can distort patterns of political expression observed during electoral campaigns. We conduct an experiment in Brazil, which predominantly corroborates predictions about declared support and clientelism.
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null (Ed.)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.more » « less
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Political news is often slanted toward its publisher’s ideology and seeks to influence readers by focusing on selected aspects of contentious social and political issues. We investigate political slants in news and their influence on readers by analyzing election-related news and reader reactions to the news on Twitter. To this end, we collected election-related news from six major US news publishers who covered the 2020 US presidential elections. We computed each publisher’s political slant based on the favorability of its news toward the two major parties’ presidential candidates. We found that the election-related news coverage shows signs of political slant both in news headlines and on Twitter. The difference in news coverage of the two candidates between the left-leaning (LEFT) and right-leaning (RIGHT) news publishers is statistically significant. The effect size is larger for the news on Twitter than for headlines. And, news on Twitter expresses stronger sentiments than the headlines. We identified moral foundations in reader reactions to the news on Twitter based on Moral Foundation Theory. Moral foundations in readers’ reactions to LEFT and RIGHT differ statistically significantly, though the effects are small. Further, these shifts in moral foundations differ across social and political issues. User engagement on Twitter is higher for RIGHT than for LEFT. We posit that an improved understanding of slant and influence can enable better ways to combat online political polarization.more » « less
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Social media provides a critical communication platform for political figures, but also makes them easy targets for harassment. In this paper, we characterize users who adversarially interact with political figures on Twitter using mixed-method techniques. The analysis is based on a dataset of 400 thousand users' 1.2 million replies to 756 candidates for the U.S. House of Representatives in the two months leading up to the 2018 midterm elections. We show that among moderately active users, adversarial activity is associated with decreased centrality in the social graph and increased attention to candidates from the opposing party. When compared to users who are similarly active, highly adversarial users tend to engage in fewer supportive interactions with their own party's candidates and express negativity in their user profiles. Our results can inform the design of platform moderation mechanisms to support political figures countering online harassment.more » « less
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
