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.
By Huangchen Xu, Yuan Wu, Yi Chang
arXiv:2606. 14715v1 Announce Type: cross Abstract: LLM agents are increasingly used to simulate real world interactions, but it remains unclear whether simulated behaviors preserve the content patterns and interaction dynamics of real human behaviors.
By Yaoning Yu, Ye Yu, Haojing Luo, Haohan Wang
arXiv:2606. 05256v1 Announce Type: new Abstract: This study analyzes a publicly released dataset from a discontinued field experiment on Reddit's r/ChangeMyView.
By Kokil Jaidka, Saifuddin Ahmed
arXiv:2608. 16578v1 Announce Type: new Abstract: AI agents increasingly operate as part of interacting systems rather than in isolation.
By Batu El, Jinhee Paeng, Fatih Dinc, Shiye Su, Mete Erdogan, Aneesh Pappu, Haotian Ye, Wanjia Zhao, Surya Ganguli, James Zou
arXiv:2509.24877v3 Announce Type: replace
Abstract: The social science of large language models (LLMs) examines how these systems evoke mind attributions, interact with one another, and transform hum...
By Xiao Jia, Zhanzhan Zhao
The study investigates how large language model (LLM) agents influence consensus formation in mixed human‑AI groups during a collaborative description game. Three regimes emerge: low agent proportions lead to human‑led consensus, intermediate proportions disrupt convergence, and high proportions produce strong, agent‑led consensus. The resulting consensus differs in semantic grounding and communicative form, with human‑led consensus being concrete and holistic, and agent‑led consensus being abstract and geometrically segmented.
By Lin Chen, Ziyi Liu, Xia Hu, Yong Li
arXiv:2609.12444v1 Announce Type: cross
Abstract: Simulated societies of large language model agents are used to study online polarization, and separately to study collective intelligence, but the tw...
By Raad Bin Tareaf
arXiv:2506. 17467v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown significant potential to change how we write, communicate, and create, leading to rapid adoption across society.
By Weixin Liang
arXiv:2606. 30905v1 Announce Type: cross Abstract: Community Notes, a bridging-based crowd-sourced fact-checking system, has emerged as a new mechanism for moderating misleading information on social media and has been adopted by major platforms including X, Facebook, Instagram, Threads, and TikTok.
By Soham De, Isaac Slaughter, Jiawei Guo, Qiao-Yun Cheng, Jiayuan Yan, Sruti Banerjee, Martin Saveski
arXiv:2607. 15006v1 Announce Type: cross Abstract: The broad adoption of Artificial Intelligence (AI), especially Generative AI, raises pressing questions about how users interact with these systems to produce new content.
By C\'elina Treuillier, Denis Lalanne
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.
By Sagar Kumar, Lawrence Swaminathan Xavier Prince, Julia Mendelsohn, Brooke Foucault Welles, Nicholas W. Landry
arXiv:2606. 27234v1 Announce Type: cross Abstract: AI nudification uses generative models to create synthetic non-consensual sexually explicit imagery (SNEACI) of real individuals.
By Chi Cui, Yixin Wu, Yang Zhang