Julia Jose

Julia Jose, Meghna Manoj Nair, Rachel Greenstadt

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.

Julia Jose, Rachel Greenstadt

Propagandists use rhetorical devices that rely on logical fallacies and emotional appeals to advance their agendas. Recognizing these techniques is key to making informed decisions. Recent advances in Natural Language Processing (NLP) have enabled the development of systems capable of detecting manipulative content. In this study, we look at several Large Language Models and their performance in detecting propaganda techniques in news articles. We compare the performance of these LLMs with transformer-based models. We find that, while GPT-4 demonstrates superior F1 scores (F1=0.16) compared to GPT-3.5 and Claude 3 Opus, it does not outperform a RoBERTa-CRF baseline (F1=0.67). Additionally, we find that all three LLMs outperform a MultiGranularity Network (MGN) baseline in detecting instances of one out of six propaganda techniques (name-calling), with GPT-3.5 and GPT-4 also outperforming the MGN baseline in detecting instances of appeal to fear and flag-waving.

Julia Jose, Ritik Roongta, Rachel Greenstadt

Despite their wide-ranging benefits, LLM-based agents deployed in open environments can be exploited to produce manipulative material. In this study, we task LLMs with propaganda objectives and analyze their outputs using two domain-specific models: one that classifies text as propaganda or non-propaganda, and another that detects rhetorical techniques of propaganda (e.g., loaded language, appeals to fear, flag-waving, name-calling). Our findings show that, when prompted, LLMs exhibit propagandistic behaviors and use a variety of rhetorical techniques in doing so. We also explore mitigation via Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and ORPO (Odds Ratio Preference Optimization). We find that fine-tuning significantly reduces their tendency to generate such content, with ORPO proving most effective.