Measuring Affective Polarization in Online Spaces with Language Models
Outgroup Animosity Has Risen for Politicians, Journalists, and a Sample of Partisan Users on Twitter and Reddit [Archived]
Research
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.
More Research
New Social Forms
Exploring new rituals, formats, and structures for coming together
Pilots & Programs
Storyline
An auditable AI framework for tracing competing narratives across podcasts, conversations, and news
Research
AI-Assisted Conversation Highlighting
Discovering semantically or emotionally salient moments in spoken discourse using LLMs
Research
realtalk@Boston
A new civic infrastructure in Boston grounded in dialogue as a way to building “civic muscle” of democracy
Pilots & Programs
Anthology
An AI interface that turns raw conversation audio into interactive maps
Research
Agora
A training ground to practice consensus-finding with real human perspectives
Research
