2026· International Conference on Language Resources and Evaluation· pp. 11152-11171· 1 citation· 43 references
Computer Science
TL;DR
A dual-level evaluation framework to assess LLM-based agents at both the individual and collective levels is proposed, finding that while agents capture broad partisan orientations, they underestimate within-group variability and reproduce stereotypical ideological biases.
A systematic comparison using a naturally occurring online persuasion corpus in which original posters explicitly verify whether a reply changed their view is conducted, highlighting the risk of treating LLM judgments as faithful proxies for human belief updating and point to structural differences in how LLMs and huma...
Social media platforms mediate how billions form opinions and engage with public discourse. As autonomous AI agents increasingly participate in these spaces, understanding their behavioral fidelity becomes critical for platform governance and democratic resilience. Previous work demonstrates that LLM-powered agents can...
Ljubiša Bojić, Alexander Felfernig, Bojana M. Dinić et al.· Scientific Reports· 0 citations
The present study profiled eight Serbian participants through a questionnaire, a deep interview, and a written self-presentation, recorded their reactions to sixty-eight social media posts, and asked four language models to predict those reactions under five prompt conditions varying profile content and instruction sty...
Ljubiša Bojić, Tijana Stanić, Joerg Matthes et al.· 0 citations
Conversational agents increasingly interact with children, yet evidence on how their design shapes children's susceptibility to persuasion comes almost entirely from the lab. We report a $2\times2$ randomized field experiment embedded in a public German Santa Claus telephone hotline. Children's calls were randomly rout...
Thilo Tamme, David Steck, Anton Hantel Technical University of Munich et al.· 0 citations
Understanding the dynamics of stance change on social media is crucial for addressing polarization and information integrity, yet observational studies face challenges including limited experimental control, restricted data access, and algorithmic confounds. We leverage Generative Agent-Based Modeling (GABM)—a novel si...
Valerio La Gatta, Gian Marco Orlando, Marco Perillo et al.· Proceedings of the 37th ACM...· 0 citations
Large Language Model (LLM)-based agents are increasingly used as proxies for human participants in social science research, yet it remains unclear whether they can faithfully simulate diverse and conflicting human value systems. We present a World Values Survey (WVS)-grounded simulation framework where culturally diver...
