LLM生成假新闻让真新闻排名下降，破坏新闻生态真实性。
LLM-Generated Fake News Induces Truth Decay in News Ecosystem: A Case Study on Neural News Recommendation
- 构建仿真流程与5.6万条假新闻数据集，模拟推荐系统中的影响。
- 发现真新闻在推荐排序中被假新闻逐步取代，出现‘真相衰减’。
- 从熟悉度和困惑度角度解释机制，适合关注信息生态的学者参考。
在线假新闻治理正面临大语言模型（LLMs）恶意生成假新闻的新挑战。尽管已有研究显示单条假新闻难以检测，但其大规模发布对新闻生态的影响仍不明确。本研究构建了仿真管道与包含约5.6万条不同类型生成新闻的数据集，探究LLM生成假新闻在神经新闻推荐系统中的影响。结果揭示了一种‘真相衰减’现象：随着假新闻参与推荐，真实新闻在排序中的优势逐渐丧失。我们从熟悉度角度解释该现象，并发现困惑度与新闻排名呈正相关。最后讨论了潜在威胁并提出应对策略，呼吁各方共同维护新闻生态完整性。
原文摘要 · Abstract (English)
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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