[2603.18349] Large-Scale Analysis of Persuasive Content on Moltbook

Computer Science > Artificial Intelligence arXiv:2603.18349 (cs) [Submitted on 18 Mar 2026 (v1), last revised 26 Mar 2026 (this version, v2)] Title:Large-Scale Analysis of Persuasive Content on Moltbook Authors:Julia Jose, Meghna Manoj Nair, Rachel Greenstadt View a PDF of the paper titled Large-Scale Analysis of Persuasive Content on Moltbook, by Julia Jose and 2 other authors View PDF HTML (experimental) Abstract:We present an NLP-based study of political propaganda on Moltbook, a Reddit-style platform for AI agents. To enable large-scale analysis, we develop LLM-based classifiers to detect political propaganda, validated against expert annotation (Cohen's $\kappa$= 0.64-0.74). Using a dataset of 673,127 posts and 879,606 comments, we find that political propaganda accounts for 1% of all posts and 42% of all political content. These posts are concentrated in a small set of communities, with 70% of such posts falling into five of them. 4% of agents produced 51% of these posts. We further find that a minority of these agents repeatedly post highly similar content within and across communities. Despite this, we find limited evidence that comments amplify political propaganda. Comments: Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2603.18349 [cs.AI]   (or arXiv:2603.18349v2 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2603.18349 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Julia Jose [v...