A Closer Look at Multidimensional Online Political Incivility
Sagi Pendzel, Nir Lotan, Alon Zoizner, Einat Minkov
Abstract
Toxic online political discourse has become prevalent, where scholars debate about its impact to Democratic processes. This work presents a large-scale study of political incivility on Twitter. In line with theories of political communication, we differentiate between harsh ‘impolite’ style and intolerant substance. We present a dataset of 13K political tweets in the U.S. context, which we collected and labeled by those categories using crowd sourcing. Our dataset and results shed light on hostile political discourse focused on partisan conflicts in the U.S. The evaluation of state-of-the-art classifiers illustrates the challenges involved in political incivility detection, which often requires high-level semantic and social understanding. Nevertheless, performing incivility detection at scale, we are able to characterise its distribution across individual users and geopolitical regions, where our findings align and extend existing theories of political communication. In particular, we find that roughly 80% of the uncivil tweets are authored by 20% of the users, where users who are politically engaged are more inclined to use uncivil language. We further find that political incivility exhibits network homophily, and that incivility is more prominent in highly competitive geopolitical regions. Our results apply to both uncivil style and substance.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext c7bd906f-b60c-4a13-a7ee-28675cd7fb3bCited by top-tier papers1
Ask how each one uses itBuilds on6
- Deberta: decoding-Enhanced Bert with Disentangled AttentionPengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu ChenICLR 2021 · 3,729 citations
- Latent Hatred: A Benchmark for Understanding Implicit Hate SpeechMai ElSherief, Caleb Ziems, David Muchlinski, Vaishnavi Anupindi et al.EMNLP 2021 · 159 citations
- CoSyn: Detecting Implicit Hate Speech in Online Conversations Using a Context Synergized Hyperbolic NetworkSreyan Ghosh, Manan Suri, Purva Chiniya, Utkarsh Tyagi et al.EMNLP 2023 · 9 citations
- Understanding Politics via Contextualized Discourse ProcessingRajkumar Pujari, Dan GoldwasserEMNLP 2021 · 6 citations
- ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech DetectionThomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap et al.ACL 2022
Related papers
- Characterizing Twitter Users Who Engage in Adversarial Interactions against Political CandidatesYiqing Hua, Mor Naaman, Thomas RistenpartCHI 2020 · 33 citations
- Twits, Toxic Tweets, and Tribal Tendencies: Trends in Politically Polarized Posts on TwitterHans W. A. Hanley, Zakir DurumericCSCW 2025
- Navigating Multidimensional Ideologies with Reddit's Political Compass: Economic Conflict and Social AffinityErnesto Colacrai, Federico Cinus, Gianmarco De Francisci Morales, Michele StarniniWWW 2024 · 7 citations
- The Unwanted Dissemination of Science: The Usage of Academic Articles as Ammunition in Contested Discursive Arenas on TwitterRichard Zhang, Emoke-Ágnes HorvátCSCW 2025 · 1 citation
- The Structure of Toxic Conversations on TwitterMartin Saveski, Brandon Roy, Deb RoyWWW 2021 · 111 citations
