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

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arxiv 2504.20013 v2 pith:7PZZGXOH submitted 2025-04-28 cs.CL cs.CYcs.IR

Beizhe Hu , Qiang Sheng , Juan Cao , Yang Li , Danding Wang This is my paper · ORCID
classification cs.CLcs.CYcs.IR
keywords newsfakellm-generateddecayrecommendationtruthchallengeecosystem
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

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