Polarized Online Discourse on Abortion: Frames and Hostile Expressions among Liberals and Conservatives
Not on the map. Why?
Abstract (arXiv)
Abortion has been one of the most divisive issues in the United States. Yet, missing is comprehensive longitudinal evidence on how political divides on abortion are reflected in public discourse over time, on a national scale, and in response to key events before and after the overturn of Roe v Wade. We analyze a corpus of over 3.5M tweets related to abortion over the span of one year (January 2022 to January 2023) from over 1.1M users. We estimate users' ideology and rely on state-of-the-art transformer-based classifiers to identify expressions of hostility and extract five prominent frames surrounding abortion. We use those data to examine (a) how prevalent were expressions of hostility (i.e., anger, toxic speech, insults, obscenities, and hate speech), (b) what frames liberals and conservatives used to articulate their positions on abortion, and (c) the prevalence of hostile expressions in liberals and conservative discussions of these frames. We show that liberals and conservatives largely mirrored each other's use of hostile expressions: as liberals used more hostile rhetoric, so did conservatives, especially in response to key events. In addition, the two groups used distinct frames and discussed them in vastly distinct contexts, suggesting that liberals and conservatives have differing perspectives on abortion. Lastly, frames favored by one side provoked hostile reactions from the other: liberals use more hostile expressions when addressing religion, fetal personhood, and exceptions to abortion bans, whereas conservatives use more hostile language when addressing bodily autonomy and women's health. This signals disrespect and derogation, which may further preclude understanding and exacerbate polarization.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 5 of its 79 references that are in the map. First posted on arXiv 28 Nov 2023.
Builds on
5 of 79 references.
References · 5
- 2020TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classificationcited by 417
- 2021SpanEmo: Casting Multi-label Emotion Classification as Span-predictioncited by 37
- 2023#RoeOverturned: Twitter Dataset on the Abortion Rights Controversycited by 14
- 2023Pandemic Culture Wars: Partisan Differences in the Moral Language of COVID-19 Discussionscited by 6
- 2020Weak ties strengthen anger contagion in social mediacited by 2
Built on by
Citing papers per year: 2024: 2; 2025: 1; 2026: 1.
4 papers.
- 2024Whose Emotions and Moral Sentiments Do Language Models Reflect?cited by 20
- 2024SoMeR: Multi-View User Representation Learning for Social Mediacited by 0
- 2025Gendered Divides in Online Discussions about Reproductive Rightscited by 0
- 2026Mapping Election Toxicity on Social Media across Issue, Ideology, and Psychosocial Dimensionscited by 0
- 2026Mapping Election Toxicity on Social Media across Issue, Ideology, and Psychosocial Dimensions18 Apr 2026
- 2025Gendered Divides in Online Discussions about Reproductive Rights7 Jul 2025
- 2024SoMeR: Multi-View User Representation Learning for Social Media2 May 2024
- 2024Whose Emotions and Moral Sentiments Do Language Models Reflect?16 Feb 2024