An overbearing mother-in-law, a passive aggressive roommate, and a confusing first date are a few examples of people who you might read about — then judge — on one of Reddit’s most popular forums: Am I the asshole?
New research from the University of Maryland’s Computational Linguistics and Information Processing (CLIP) Lab offers a fresh perspective on one of social media’s most debated trends: judging Reddit’s r/AmItheAsshole posts. The work also explores how the psychology of intergroup bias plays out in online spaces.
In this online discussion forum, described as “a catharsis for the frustrated moral philosopher in all of us, and a place to finally find out if you were wrong in an argument,” users anonymously share personal dilemmas and ask the community to judge whether they are to blame in interpersonal conflicts. The majority of stories are about family and romantic relationships, followed by friendships, work and strangers. Reddit users engage by commenting and voting on posts. These debates often expand beyond Reddit to video-based social media platforms, where creators read the posts aloud and ask their own followers to weigh in.
The CLIP team began the project by searching for a real-world dataset to test research questions about how intergroup bias — the tendency to side with and empathize more with members of one’s own social groups — plays out online. Previous research in this area has only been in controlled lab settings, but with more of our social interactions happening online and fewer in person, the researchers saw a need to test these types of human behavior theories in online spaces.
The UMD researchers chose to focus on age and gender as social identities. But since not all Reddit commenters publicly disclose those demographics, the research team created a database and used a large language model to narrow down roughly half a million posts and 37 million comments to a usable dataset. They also developed a system for measuring empathy in the comments.
Their analysis of 126,000 comments on 36,163 r/AmItheAsshole posts shows that commenters who shared the poster’s age or gender were more likely to issue a “Not the Asshole” verdict and express greater empathy in their responses.The researchers then examined whether conflicts between people from different groups affected how commenters responded to people who posted about their personal dilemmas. They found that people tended to take sides, and the difference in empathy toward someone from their own group versus someone from another group was stronger when a post described a conflict between groups.
However, since Reddit users tend to be younger overall, the sample limited the researchers’ ability to examine differences across a broad age range. Based on the age distribution in their sample, the researchers chose ages 20, 26 and 38 to compare and structure their data.
Yu “Hope” Hou, a fourth-year computer science Ph.D. student and lead author of the paper describing the work, was surprised to find a different pattern of intergroup bias among older commenters—they were not as likely as younger commenters to agree with someone from their age group when issuing the verdict. The older the commenter, the less age appeared to matter.
The researchers speculate that older users’ life experience may cause them to be less influenced by their own age group when making judgments, and better able to relate to people of other ages because they were once that age themselves, as suggested by prior literature. Yet more research is needed to test these theories.
Hou is excited about the many future directions her research could take.
“There are so many social identities we can study, and so many identities within one person. For example, I’m Chinese, an international student, a Ph.D. student, and female,” Hou said. “Which social identities are people most motivated to use, and why? Is it influencing their decision to choose to understand—or choose not to understand—the other person?”
Hou collaborated on this project with her advisers Rachel Rudinger, an associate professor of computer science, and Hal Daumé III, a professor of computer science and director of both the Artificial Intelligence Interdisciplinary Institute at Maryland (AIM) and the Institute for Trustworthy AI in Law & Society (TRAILS).
Given AI’s increasing influence in online communication, it is important to understand the role of intergroup bias in these settings, especially as AI systems learn human behaviors and take on roles such as a poster, commenter or mediator, noted the researchers.
“AI systems are increasingly taking a part in online communities, and these systems are extremely good at picking up every subtle pattern in human behavior,” Daumé said. “We found that when strangers judge an everyday conflict, they're a little more likely to side with someone who shares their gender or is close to their age – very clearly opening the door for AI systems to do the same, and likely exacerbate that behavior.”
Both Rudinger and Daumé have appointments in the University of Maryland Institute for Advanced Computer Studies (UMIACS), which provided computing clusters for the project. Without those resources, the project would have required substantially more time and money, Hou said.
Hou first presented the work in April at the Mid-Atlantic Student Colloquium on AI, Language and Learning (MASC-ALL) 2026 in Washington D.C., where she received a Best Oral Presentation award. In July, she presented it at the 7th Workshop on Natural Language Processing and Computational Social Science during the 64th Annual Meeting of the Association for Computational Linguistics in San Diego. An updated paper describing the research has been accepted for presentation at next year’s 21st International AAAI Conference on Web and Social Media in Scotland.
—Story by Maria Herd, UMIACS communications group