The study examines how intergroup identity—specifically shared gender or age—affects commenters’ social judgments and empathy in the r/AmITheAsshole subreddit. Analyzing 126,716 comments on 36,163 posts, the authors find that ingroup commenters are more likely to give a ‘Not the Asshole’ verdict and display greater text-based understanding, as measured by large language models. The research also notes age differences among commenters and shows that the ingroup effect is moderated by the relationship between the poster and another party mentioned in the conflict.
arXiv:2605.27388v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used as proxies for computational social analysis, yet faithfully representing the "thick description...
The paper investigates how large language model agents on the open platform Moltbook represent humans, focusing on human-directed stereotypes. Using an annotation framework with four dimensions—morality, friendliness, competence, and autonomy—and a subtype scheme for other attributions, the study finds that competence is the dominant evaluation, while many other attributions describe humans as epistemic, cultural, or embodied subjects. The authors also analyze how these representations appear in narrative contexts and platform-level circulation, noting that community feedback is better explained by exposure, author visibility, and content selection rather than stable insider–outsider dynamics.
arXiv:2608.21242v1 Announce Type: new Abstract: As conversational companions, large language models (LLMs) often have access to users' emotional states. We study how this affective context modulates...
The Illinois Social Attitudes Aggregate Corpus (ISAAC) is an open, modular corpus comprising over 527 million English‑language Reddit posts from 2007 to 2023, curated for relevance to six social group distinctions—race, sexuality, age, ability, body weight, and skin tone. A multi‑step, human‑audited filtering pipeline keeps irrelevant content below 10% overall and per group, while each post receives algorithmic annotations of user home region and a suite of validated semantic labels such as moralization, sentiment, emotion, and linguistic generalization. ISAAC’s publicly available, reproducible pipeline enables cross‑category comparisons, high‑precision tracking of long‑term temporal shifts, and spatial mapping of public opinion and policy outcomes, and can be extended to new platforms, languages, and social categories via both point‑and‑click and programmatic interfaces.
arXiv:2610.33097v1 Announce Type: new Abstract: As polarization grows on social media, understanding how political memes shape online narratives has become increasingly important. Previous research h...
arXiv:2609.26579v1 Announce Type: new Abstract: A central concern with language models is sycophancy: their tendency to defer to users' views at the expense of independent substantive judgment. In pa...
The paper investigates which demographic attributes large language models (LLMs) default to when annotating text without explicit demographic cues. By comparing non‑demographic, placebo‑conditioned, and demographic‑conditioned prompts on politeness and offensiveness tasks in the POPQUORN dataset, the authors find that LLMs exhibit notable gender, race, and age influences in their annotations. This contrasts with earlier studies that reported no such effects, highlighting the importance of considering demographic bias in LLM‑based annotation workflows.
arXiv:2608.29803v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as proxies for human participants in social simulations, yet whether they update their beliefs...
The paper investigates how shared community affiliations, measured via Bluesky starter packs, correlate with common ground between users. By analyzing 191,648 user pairs, it finds that lexical similarity—used as a proxy for common ground—increases monotonically with the number of shared starter packs, especially when those packs represent distinct topical communities. The study also shows that this effect is independent of network proximity, indicating that community membership is a distinct, measurable carrier of common ground.
The paper reports the first large‑scale empirical comparison of AI‑agent and human online communities, analyzing 73,899 Moltbook and 189,838 Reddit posts across five matched communities. It finds that Moltbook shows extreme participation inequality (Gini = 0.84 vs. 0.47) and high cross‑community author overlap (33.8% vs. 0.5%). Linguistically, AI‑generated content is emotionally flattened, more assertive than exploratory, and socially detached, leading to community‑level homogenization that is largely a structural artifact of shared authorship. At the individual level, AI agents are more identifiable than human users due to outlier stylistic profiles amplified by their extreme posting volume.
The paper examines how six foundational theories of intergroup hostility—boundary construction, threat construction, scapegoating, negative evaluation, dehumanization, and action orientation—manifest in online discourse. Using 2.86 million posts from TikTok, Truth Social, and Twitter/X during the 2024 U.S. presidential election, the authors model these mechanisms within a common empirical framework and find that boundary and threat construction anchor the system, with a regular temporal ordering of rhetorical strategies. The study maps theoretical frameworks onto real-world discourse, offering a clearer empirical basis for computational social science to model intergroup hostility beyond single-label detection.
arXiv:2607. 27232v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly shaping how we consume information and form our worldview.