arXiv:2606. 06027v1 Announce Type: cross Abstract: Community-conditioned language model adaptation requires choices about data collection, community definition, and evaluation that are currently made independently in each study, making it hard to compare assumptions or reuse artifacts.
By Amirhossein Ghaffari, Ali Goodarzi, Huong Nguyen, Simo Hosio, Lauri Lov\'en, Ekaterina Gilman
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...
By Nuan Wen, Chanbin Lim, Xuezhe Ma
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.
By Yu Hou, Hal Daum\'e III, Rachel Rudinger
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.
By Agam Goyal, Olivia Pal, Hari Sundaram, Eshwar Chandrasekharan, Koustuv Saha
The paper investigates how internet slang spreads across Reddit communities by combining social network analysis with linguistic context. Using large language models as scalable annotators, the authors create a benchmark for detecting slang usage and then model its adoption and diffusion. Findings reveal that users with higher bridging capital promote slang spread, while those with higher bonding capital hinder it, and that broader contextual usage delays new user adoption.
By Xiaoning Wang, Ted Underwood, Zhewei Sun
The study examines how the release of ChatGPT-3.5 affected collective knowledge on Stack Overflow, analyzing over two million questions from 2020 to 2025. It finds that easy questions declined sharply while difficult ones increased, with rising code complexity. Data-rich topics lost share of questions, whereas data-scarce ones gained, and these patterns held across multiple programming languages.
By Myokyung Han, Taegyoon Kim, Jinhyuk Yun, Lanu Kim