用动态知识更新提升假新闻检测准确率
A Dynamic Knowledge Update-Driven Model with Large Language Models for Fake News Detection
- 基于知识图谱实现新闻事件的持续知识更新
- 在两个真实数据集上达到最优检测效果
- 适合需要实时更新信息的新闻审核场景
随着互联网和社交媒体的快速发展，从海量复杂信息中辨别可信新闻成为重大挑战。由于新闻事件具有突发性和不稳定性，其真实性标签可能随事件发展而变化，因此获取最新事件进展对假新闻检测至关重要。现有方法采用检索增强生成来填补知识空白，但存在检索内容可信度不足、噪声信息干扰等问题。本文提出一种动态知识更新驱动的假新闻检测模型DYNAMO，利用知识图谱实现新知识的持续更新，并与大语言模型结合，完成新闻真伪判断和新知识正确性验证双重任务，解决新知识真实性保障与新闻语义深度挖掘两大难题。具体而言，首先构建新闻领域专用知识图谱；然后使用蒙特卡洛树搜索对复杂新闻进行分步解析与验证；最后从已验证的真实新闻文本及推理路径中提取并更新新知识。实验结果表明，DYNAMO在两个真实世界数据集上均取得最佳性能。
原文摘要 · Abstract (English)
As the Internet and social media evolve rapidly, distinguishing credible news from a vast amount of complex information poses a significant challenge. Due to the suddenness and instability of news events, the authenticity labels of news can potentially shift as events develop, making it crucial for fake news detection to obtain the latest event updates. Existing methods employ retrieval-augmented generation to fill knowledge gaps, but they suffer from issues such as insufficient credibility of retrieved content and interference from noisy information. We propose a dynamic knowledge update-driven model for fake news detection (DYNAMO), which leverages knowledge graphs to achieve continuous updating of new knowledge and integrates with large language models to fulfill dual functions: news authenticity detection and verification of new knowledge correctness, solving the two key problems of ensuring the authenticity of new knowledge and deeply mining news semantics. Specifically, we first construct a news-domain-specific knowledge graph. Then, we use Monte Carlo Tree Search to decompose complex news and verify them step by step. Finally, we extract and update new knowledge from verified real news texts and reasoning paths. Experimental results demonstrate that DYNAMO achieves the best performance on two real-world datasets.
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