Large-Scale Analysis of Political Propaganda on Moltbook
This study addresses the problem of understanding political propaganda in AI agent communities for researchers and platform moderators, but it is incremental as it applies existing NLP methods to a new domain.
The authors tackled the problem of analyzing political propaganda on the AI agent platform Moltbook by developing LLM-based classifiers validated with expert annotation (Cohen's κ=0.64-0.74). They found that political propaganda accounts for 1% of all posts and 42% of political content, with 4% of agents producing 51% of these posts and limited evidence of comment amplification.
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 $κ$= 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.