2023/08/29 by Suyash Fulay, Deb Roy, Fulay, Suyash +1
Computer Science · Social Sciences · #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection #Media Studies and Communication #Social Media and Politics #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.2308.15556
openalex publication_date 2023/08/29 · openalex created_date 2023/09/02 · openalex updated_date 2026/07/28
Using language models, we analyze a sample of 67 million tweets and 30 million Reddit comments referencing a set of 215 political entities from 2010-2023 from partisan users, journalists, and politicians. Our analysis indicates outgroup animosity has increased consistently in our sample, with newer cohorts of users expressing higher levels of animosity than previous ones. Moreover, a small fraction of users are responsible for a disproportionate share of this negative content. We observe systematic differences in topic-level outgroup affect across political orientations: right-leaning users are twice as likely to exhibit outgroup animosity when discussing immigration, while left-leaning users show heightened outgroup animosity when discussing healthcare. On Twitter, U.S. politicians on the left exhibit more outgroup animosity than partisan users in our sample, but in the past four years, politicians on the right have experienced the sharpest rise in outgroup animosity, surpassing journalists, media, and partisan users. On Reddit, a small number of communities account for a large share of polarizing rhetoric, with the rise and eventual ban of r/TheDonald significantly shaping polarizing discourse on the right.