Polarized Online Discourse on Abortion: Frames and Hostile Expressions among Liberals and Conservatives
Ashwin Rao, Rong-Ching Chang, Qiankun Zhong, Kristina Lerman and, Magdalena Wojcieszak

TL;DR
This study analyzes over 3.5 million tweets over a year to examine how liberals and conservatives in the US discuss abortion, revealing polarization in framing and hostility that intensifies around key events post-Roe v. Wade overturn.
Contribution
It provides a comprehensive longitudinal analysis of online abortion discourse, highlighting distinct framing and hostility patterns among liberals and conservatives using transformer-based classifiers.
Findings
Liberals and conservatives mirror each other's hostility levels.
Distinct frames are used by each side to discuss abortion.
Hostile expressions increase around key events post-Roe v. Wade.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsHate Speech and Cyberbullying Detection · Reproductive Health and Contraception
