Source-linked AI summary
Intergroup Bias Predicts Expressions of Social Judgment and Empathy in the r/AmITheAsshole Reddit Community
Yu Hou, Hal Daumé III, Rachel Rudinger
TL;DR
Few studies have examined intergroup effects in large-scale, real-life online interactions while controlling for confounds. This paper analyzes AITA comments using self-reported gender and age, finding that ingroup relationships are associated with more NTA verdicts and greater text-based empathy.
Problem
How intergroup empathy manifests in naturally occurring online interactions remains understudied, particularly in large-scale analyses that control for possible confounds.
Source: §1 · p. 1Method
The authors analyze AITA comments using self-reported gender and age, LLM-predicted features, and logistic mixed-effects models controlling for identities, post features, and group-level variance.
Source: §1 · p. 2Results
Gender and age ingroup relationships are associated with more NTA verdicts and text-based empathy; shared gender corresponds to a 3.18 pp increase in NTA verdict probability and a 2.38 pp increase in emotional reactions.
Source: §1 · p. 2Takeaways & Limitations
The authors argue that intergroup bias should be considered in online discourse, including when AI serves as a poster, commenter, or mediator.
Source: §1 · p. 2The study relies on self-disclosed identities being accurate and perceived as stated, and the authors acknowledge limitations of using LLMs as data annotators.
Source: §5 · p. 8
Abstract
from arXiv · showhide
People share and understand others' experiences differently based on their social identities; often, people empathize more when the other person belongs to their ingroup compared to an outgroup. While this intergroup empathy gap is well-studied in social psychology, it remains unclear how it manifests in naturally occurring online social interactions. To this end, we measure how commenters express social judgments, as an indirect outcome of their understanding towards the posters, and empathy in text, based on whether commenters share the poster's gender or are of similar ages. We focus on posts where the poster self-reports these demographics, and analyze 126,716 comments associated with 36,163 posts from 18,111 commenters in r/AmItheAsshole, while controlling for commenter and poster group identities, post features (topic, length, and score), and both commenter- and post-level variance. Our results show that when commenters and posters are ingroup, in either gender or age, commenters are more likely to provide a Not the Asshole verdict and show greater text-based understanding as measured by LLMs. We also observe that older commenters behave differently from younger ones. Furthermore, in a subset of posts where the gender and age of the other party in the conflict are explicitly mentioned, we find that commenter-poster ingroup effects are moderated by the group relationships between the poster and the other party when commenters make social judgments. Our work highlights the importance of intergroup context and social group nuances in understanding online discourse.
1 Introduction
The paper examines whether commenter–poster gender and age relationships are associated with social judgments and expressed understanding in online interactions. In r/AmItheAsshole comments, gender and age ingroup relationships correspond to more NTA verdicts and greater text-based empathy.
1 Introduction
- Few studies have tested intergroup effects in large-scale, real-life online interactions while controlling for possible confounds.The paper frames this gap through the intergroup empathy gap: people may show more understanding toward ingroup members than outgroup members.Source: §1 · p. 1
- The study treats verdicts as an indirect outcome of understanding, while text-based empathy is measured more directly in commenters’ open-ended language.The authors caution that a judgment does not necessarily reflect understanding; empathy is only one possible factor influencing a verdict.Source: §1 · p. 2
- The study asks whether gender or age ingroup status predicts NTA verdicts and text-based empathy, and whether poster–other-party relationships moderate those effects.It also asks whether social-judgment effects differ between younger and older commenters.Source: §1 · p. 2
- The analysis uses 126,716 comments from 36,163 posts and controls for poster and commenter identities, post features, and post- and commenter-level variance.A subset of 71,887 comments from 20,464 posts supports analyses of the poster–other-party relationship.Source: §1 · p. 2
- Shared gender and narrower age gaps are associated with more NTA verdicts and greater expressed empathy; the age effects vary with commenter age and the poster–other-party relationship.For RQ3, the reported NTA effects occur when the commenter shares the poster’s gender but not the other party’s, or when the poster–other-party age gap is large.Source: §1 · p. 2
- The authors argue that intergroup bias should be considered when AI participates in online communities as a poster, commenter, or mediator.They present the observed relationships as correlations in the AITA community.Source: §1 · p. 2
2 Background and Related Work
Prior work studies AITA posts as moral and social cases and examines how social context appears in language. This paper positions its contribution as testing poster–commenter intergroup effects at scale while controlling for other factors.
2 Background and Related Work
- Prior AITA research examines topics and moral dimensions, evaluates LLMs, and analyzes verdicts and blame.Examples include topic and moral-dimension studies (Nguyen et al. 2022; Nguyen et al. 2024), LLM evaluations (Sachdeva and van Nuenen 2025; Cheng et al. 2025b), and analyses of judgments and blame (Lourie, Le Bras, and Choi 2021; Zhou, Smith, and Lee 2021).Source: §2 · p. 2
- Language research considers social context and relationships, while social-media text has been studied for gender attitudes, empathy, empowerment, and condescension.The cited work also includes people’s perspectives and intergroup or interpersonal relationships in language.Source: §2 · pp. 2–3
- The authors position their study as the first to examine poster–commenter intergroup effects at scale while controlling for other factors.They connect AITA narratives to real-life social scenarios in which users comment from their own judgments, experiences, and social roles.Source: §2 · p. 3
3 Materials and Methods
The study builds a filtered sample of AITA interactions with self-reported demographic information, defines comment-level judgment and empathy outcomes, and estimates ingroup associations using mixed-effects models. Its methods account for demographic baselines, post features, and repeated observations across posts and commenters.
3.1 Data and Data Processing
- From 456,200 AITA posts, regex and LLM extraction retained 119,063 posts with mutually agreed self-reported gender and age.The resulting user database included 105,376 users with known gender and year of birth.Source: §3.1 · pp. 3–4
- The final analysis includes 126,716 top-level NTA or YTA comments from 18,111 adult commenters and 36,163 posts, after applying demographic and activity filters.The selected commenters and posters had known gender and age, and commenters differed from posters.Source: §3.1 · p. 4
3.2 Feature Preparation
- Models control poster and commenter demographics, post length, engagement score, topic, and repeated observations at the post and commenter levels.Topics cover romance, family, friendship, work/school, public strangers, and society; public and society are merged for regression.Source: §3.2 · p. 4, §3.3 · p. 5
- Gender ingroup indicates matching poster and commenter gender, while age ingroup is the negative absolute log-age gap, with each unit narrowing the age ratio by a factor of 1.5.The age measure is defined so that zero corresponds to the same age.Source: §3.2 · pp. 4–5
- The outcomes combine binary NTA/YTA verdicts with text-based emotional-reaction and interpretation scales, whose positive ratings indicate expressed empathy.The empathy scales adapt EPITOME questions and use LLM annotations; author checks reported F1 scores of 0.73 for Emotional Reactions and 0.92 for Interpretations.Source: §3.2 · p. 5
3.3 Regression Analysis
- Mixed-effects logistic models include an age-by-commenter-age interaction and crossed post/commenter intercepts; effects are summarized as population-level percentage-point marginal effects.The interaction tests whether age ingroup patterns differ by commenter age.Source: §3.3 · pp. 5–6
4 Results
In the AITA community, gender and age ingroup bias correlates with social judgments and text-based empathy, with social-judgment effects depending on the poster’s relationship to the other party. The age pattern differs by outcome: older commenters show weaker judgment bias but stronger expressed empathy bias.
4.1 RQ1: Ingroup Effects in Social Judgments
- Sharing the poster’s gender is associated with a 3.18 pp increase in commenters’ probability of giving an NTA verdict.The increase compares gender outgroup with ingroup relationships.Source: §4.1 · p. 6
- A 1.5-fold narrowing of the commenter-poster age ratio is associated with 1.03 pp greater likelihood of an NTA verdict, with effects weaker among older commenters.The reported age-distance estimate is 1.03 pp (95% CI: [0.49, 1.57]; p < 0.001); the age pattern becomes smaller and statistically non-significant as commenters get older.Source: §4.1 · p. 6, Figure 5 · p. 6
4.2 RQ2: Ingroup Effects in Expressed Text-Based Empathy
- Sharing the poster’s gender is associated with 2.38 pp more emotional reactions and 1.66 pp more interpretations in comments.Both estimates are significant (p < 0.001).Source: §4.2 · p. 6
- A 1.5-fold narrowing of the commenter-poster age ratio is associated with greater emotional reactions and interpretations in comments.The reported estimates are 1.16 pp for emotional reactions and 0.88 pp for interpretations; older commenters show stronger age ingroup bias in expressed empathy than younger commenters.Source: §4.2 · pp. 6–7, Figure 5 · p. 6
4.3 RQ3: Ingroup Effects Moderated by the Social Context
- When the poster and other party differ in gender, commenter-poster gender ingroup is associated with a 5.43 pp increase in NTA verdicts; for same-gender conflicts, the effect is non-significant.The average estimate is 3.96 pp; the same-gender estimate is -0.29 pp (p = 0.645).Source: Figure 6 · p. 7, §4.3 · p. 7
- For conflicts with a large poster–other-party age gap, commenter-poster age ingroup is associated with a 1.53 pp increase in NTA verdicts, while the average estimate is non-significant.The large-gap estimate is for OUTPA = 1.348; the average estimate is 0.38 pp (p = 0.746).Source: Figure 6 · p. 7, §4.3 · p. 7
- The context analysis uses posts where both the other party’s gender and age were explicitly extracted, yielding 71,887 comments on 20,464 posts.The model adds poster–other-party gender and age relationships and their interactions with commenter–poster ingroup measures.Source: §4.3 · p. 7
5 Discussion
The study finds gender- and age-based ingroup correlations with verdicts and text-based understanding, while age effects differ by commenter age and post context. Conclusions are bounded by identity selection, assumptions about perceived identity, LLM annotation, and the 2018–2022 data range.
5 Discussion
- Commenters sharing the poster’s gender or having a similar age were more likely to issue an NTA verdict and show greater understanding; age effects differed by commenter age and post context.The authors report these associations after controlling for post, poster, and commenter effects.Source: §5 · p. 8
- The gender ingroup effect on social judgment was larger than the age ingroup effect, while topic-category AMEs ranged from −5.56 pp for romance to −1.55 pp for work/school relative to public.The gender ingroup effect had an AME of 3.18 pp; a one-unit increase in log age distance corresponded to 1.03 pp.Source: §5 · p. 8
- Identity filters discarded a large portion of the initial data, and omitting pairs with unknown identities risks restricting results to interactions with explicitly stated, parsable identities.The authors also note that the data are dominated by binary genders and that online self-reports may be inaccurate.Source: §5 · p. 8
- The analysis assumes commenters perceive self-disclosed identities as consistent with the post and receive them as stated, though actual self-identity and perceived identity may differ.Users may misrepresent identities online, or commenters may form perceptions through other interactions.Source: §5 · p. 8
- LLM annotation has limitations; the authors use it to extend the analysis of comment and post content for exploratory purposes.They suggest more robust annotation pipelines or approaches for future work.Source: §5 · p. 8
- The final data span 2018–2022, and changing discourse norms may influence the findings.The authors suggest applying the approach to more recent data to examine how findings may have shifted.Source: §5 · p. 8
- Although the study uses publicly available data, Reddit users did not consent to research studies; the authors plan to release only anonymous, aggregated features to protect privacy.Source: §5 · p. 8
Paper Checklist
The checklist reports that the paper addresses its assumptions, research context, limitations, and implications, and documents its experimental and data practices.
Paper Checklist
- The checklist reports that the paper’s main claims and methodological approach are aligned with its contributions and scope, and that possible data artifacts are discussed.Source: Paper Checklist · p. 12
- The checklist marks hypotheses, assumptions, justifications, competing explanations, alternatives, limitations, related literature, and implications as addressed in the paper.Source: Paper Checklist · p. 12
- The checklist marks code and data, training details, error bars, compute resources, evaluation justification, and misclassification costs as discussed.Source: Paper Checklist · p. 12
- The checklist reports discussion of assets, consent, personally identifiable or offensive content, privacy-related impacts, and potential misuse.Source: Paper Checklist · pp. 12–13
A LLM Prompting Experiment Details
The appendix documents prompts for extracting post information, identifying conflict parties, and annotating empathy in comments. The authors iteratively developed prompts on a small annotated subset and treat these analyses as supplementary.
A LLM Prompting Experiment Details
- Prompts beyond user-information extraction were iteratively developed using 24 comments and 11 posts annotated by the authors, and their analyses were treated as supplementary.For user-information extraction, the authors aimed to clean up noise while relying on regex results, so LLM errors had little influence on the analysis.Source: A LLM Prompting Experiment Details · p. 13
A.1 Prompt A.1: Post User Info Extraction System Prompt
- The user-information prompt extracts poster gender and age only when directly stated in the post, avoids inference, and returns unknown when information is absent.Source: §A.1 · p. 13
A.2 Prompt A.2: Post Conflict Party & Topic System Prompt
- The conflict-party prompt identifies the OP and the primary person in conflict, extracting that person’s age and gender only when explicitly stated.Source: §A.2 · p. 13
- Relationship categories are assigned according to who the conflict person is to the OP, using six ordered categories from romance to society.When categories are unclear, the prompt directs use of the higher-priority category.Source: §A.2 · p. 14
A.4 Prompt A.4: Comment Text-Based Empathy System Prompt
- The empathy prompt scores emotional reactions toward the OP and interpretations of the OP’s experiences separately, each on a 0–2 scale.Specific interpretations include conjectures, paraphrases, or relevant similar experiences.Source: §A.4 · p. 14
- In the prompt example, condemnation and sympathy directed toward the OP’s sister do not count as empathy toward the OP, producing scores of 0 on both dimensions.Source: §A.4 · p. 15
A.5 Prompt A.5: Comment Input Prompt
- The comment-analysis prompt receives the post and comment together as its input.Source: §A.5 · p. 15