LLM-Generated Fake News Induces Truth Decay in News Ecosystem: A Case Study on Neural News Recommendation
🤖 AI Summary
This paper identifies the “truth decay” phenomenon—where authentic news items are systematically demoted in neural news recommendation systems due to competition with large language model (LLM)-generated fake news. We construct a scalable simulation pipeline containing 56K LLM-generated news articles, enabling the first formal definition and empirical validation of truth decay. Drawing on user familiarity as a key behavioral signal, we explain how fake news gains ranking advantage and uncover a significant positive correlation between news perplexity and recommendation rank. Our contributions are threefold: (1) the first quantitative formulation and measurement of truth decay rate; (2) a controllable fake news dataset and benchmark evaluation framework for truth decay analysis; and (3) a novel defense method integrating credibility-aware source enhancement and perplexity-sensitive re-ranking, which effectively mitigates truth decay while preserving recommendation accuracy.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Investigating impact of LLM-generated fake news on news ecosystems
Analyzing truth decay in neural news recommendation systems
Exploring detection and countermeasures for LLM-produced fake news
Innovation
Methods, ideas, or system contributions that make the work stand out.
Simulation pipeline for LLM-generated fake news impact
Dataset with 56k diverse fake news samples
Analysis of truth decay in neural recommendation systems
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