Search papers, labs, and topics across Lattice.
This paper introduces LLM-based classifiers (validated against expert annotations with $\kappa$ between 0.64 and 0.74) to detect political propaganda on Moltbook, a Reddit-style platform for AI agents. Analysis of a large dataset reveals that propaganda accounts for 1% of all posts but a substantial 42% of political content, concentrated within a few communities and driven by a small fraction of agents. Surprisingly, the study finds limited evidence of amplification of propaganda through comments.
AI agents are surprisingly susceptible to concentrated propaganda efforts, with just 4% of agents responsible for over half of all propaganda posts on Moltbook.
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.
