LLM-Generated Fake News Induces Truth Decay in News Ecosystem: A Case Study on Neural News Recommendation

April 28, 2025 · Declared Dead · 🏛 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

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Authors Beizhe Hu, Qiang Sheng, Juan Cao, Yang Li, Danding Wang arXiv ID 2504.20013 Category cs.CL: Computation & Language Cross-listed cs.CY, cs.IR Citations 10 Venue Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Last Checked 4 months ago

Abstract

Online fake news moderation now faces a new challenge brought by the malicious use of large language models (LLMs) in fake news production. Though existing works have shown LLM-generated fake news is hard to detect from an individual aspect, it remains underexplored how its large-scale release will impact the news ecosystem. In this study, we develop a simulation pipeline and a dataset with ~56k generated news of diverse types to investigate the effects of LLM-generated fake news within neural news recommendation systems. Our findings expose a truth decay phenomenon, where real news is gradually losing its advantageous position in news ranking against fake news as LLM-generated news is involved in news recommendation. We further provide an explanation about why truth decay occurs from a familiarity perspective and show the positive correlation between perplexity and news ranking. Finally, we discuss the threats of LLM-generated fake news and provide possible countermeasures. We urge stakeholders to address this emerging challenge to preserve the integrity of news ecosystems.