# Characterizing Twitter Users Who Engage in Adversarial Interactions against Political Candidates

arXiv: https://arxiv.org/abs/2005.04412 · HTML: https://mlsanity.com/paper/2005.04412/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2020.

## Built on by (most cited first, 6 of 6)

- [A Federated Approach to Few-Shot Hate Speech Detection for Marginalized Communities](https://mlsanity.com/paper/2412.04942/) (2024)
- [An Empirical Study of Collective Behaviors and Social Dynamics in Large Language Model Agents](https://mlsanity.com/paper/2602.03775/) (2026)
- [Bye Bye Perspective API: Lessons for Building and Governing Measurement Infrastructure](https://mlsanity.com/paper/2604.25580/) (2026)
- [Analyzing User Characteristics of Hate Speech Spreaders on Social Media](https://mlsanity.com/paper/2310.15772/) (2023)
- [Hostility Detection in UK Politics: A Dataset on Online Abuse Targeting MPs](https://mlsanity.com/paper/2412.04046/) (2024)
- [Beyond Static Benchmarks: Synthesizing Harmful Content via Persona-based Simulation for Robust Evaluation](https://mlsanity.com/paper/2604.17020/) (2026)

## Abstract (arXiv)

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.