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:2607. 29334v1 Announce Type: cross Abstract: Conversational AI developed by geopolitical rivals reaches citizens worldwide, raising concerns that it could sway public opinion or be rejected as foreign propaganda, with consequences for democratic discourse and information sovereignty.
By Ningzhi Liu, Yannic Hinrichs, Jonas R. Kunst
arXiv:2601. 05050v3 Announce Type: replace Abstract: Large language models (LLMs) have been shown to be persuasive across a variety of contexts.
By Thomas H. Costello, Kellin Pelrine, Matthew Kowal, Jasper Timm, Antonio A. Arechar, Jean-Fran\c{c}ois Godbout, Adam Gleave, David Rand, Gordon Pennycook
arXiv:2608.21389v1 Announce Type: cross
Abstract: Generative AI enables customized misinformation at scale, yet defenses remain largely reactive. We present empirical findings from a human-subject st...
By Alexander Loth, Martin Kappes, Marc-Oliver Pahl
arXiv:2608. 15746v1 Announce Type: new Abstract: We present a forensic analysis of the generation pipeline behind a recent AI-driven influence campaign.
By Benjamin Icard, Elouan Vuichard, Louis Lefebvre, Lila Sainero, Thomas Girault, Alice Breton, Tanguy Launay, Gauvain Bourgne, Morgane Casanova, Guillaume Gadek, Victor Kl\"otzer, Michel Le Nouy, Guillaume Gravier, Jean-Gabriel Ganascia, Paul \'Egr\'e
The paper investigates whether AI evaluators differentiate between an argument’s content and the source attributed to it. Using 2,976 evaluations of six fixed texts across various source attributions, the study finds that the perceived quality of an argument varies with its source, indicating source-position coherence. The authors also note that this pattern holds across topics and model configurations, and that some evaluators explicitly noted mismatches between source and position.