MPCG: Multi-Round Persona-Conditioned Generation for Modeling the Evolution of Misinformation with LLMs

ACL ARR 2025 May Submission2500 Authors

Abstract: Misinformation evolves as it spreads, shifting in language, framing, and moral emphasis to adapt to new audiences. However, current misinformation detection approaches implicitly assume that misinformation is static. We introduce MPCG, a multi-round, persona-conditioned framework that simulates how claims are iteratively reinterpreted by agents with distinct ideological perspectives. Our approach uses an uncensored large language model (LLM) to generate persona-specific claims across multiple rounds, conditioning each generation on outputs from the previous round, enabling the study of misinformation evolution. We evaluate the generated claims through human and LLM-based annotations, cognitive effort metrics (readability, perplexity), emotion evocation metrics (sentiment analysis, morality), clustering, and downstream classification. Results show strong agreement between human and GPT-4o-mini annotations, with higher divergence in fluency judgments. Generated claims require greater cognitive effort than the original claims and consistently reflect persona-aligned emotional and moral framing. Clustering and cosine similarity analyses confirm semantic drift across rounds while preserving topical coherence. Classification results reveal that commonly used misinformation detectors experience macro-F1 performance drops of up to 50\%. The code is available at https://anonymous.4open.science/r/anonymous-repo-62D1

Paper Type: Long

Research Area: Computational Social Science and Cultural Analytics

Research Area Keywords: Computational Social Science and Cultural Analytics, NLP Applications

Contribution Types: NLP engineering experiment, Reproduction study

Languages Studied: English

Submission Number: 2500