基于多视角注意力的假新闻检测模型，仅凭原文与转发链精准识别假消息。
MVAN: Multi-View Attention Networks for Fake News Detection on Social Media
- 融合文本语义与传播结构的双注意力机制
- 在两个数据集上平均准确率提升2.5%优于现有方法
- 可定位关键误导词和可疑转发用户，解释性强
社交媒体上的假新闻是当今社会广泛存在的严重问题。现有检测方法主要依赖长文本内容（如原始新闻文章和用户评论）寻找线索。本文针对更贴近现实的应用场景——仅提供源推文文本及其转发用户信息，无用户评论——提出新型神经网络模型MVAN（Multi-View Attention Networks），实现假新闻检测并提供可解释性。该模型包含文本语义注意力与传播结构注意力，能同时捕捉源推文内容与传播结构中的信息线索。两个注意力机制分别定位假新闻中的关键误导词和传播链中的可疑用户。在两个真实数据集上的实验表明，MVAN平均准确率比当前最优方法高出2.5%，且能生成合理解释。
原文摘要 · Abstract (English)
Fake news on social media is a widespread and serious problem in today's society. Existing fake news detection methods focus on finding clues from Long text content, such as original news articles and user comments. This paper solves the problem of fake news detection in more realistic scenarios. Only source shot-text tweet and its retweet users are provided without user comments. We develop a novel neural network based model, \textbf{M}ulti-\textbf{V}iew \textbf{A}ttention \textbf{N}etworks (MVAN) to detect fake news and provide explanations on social media. The MVAN model includes text semantic attention and propagation structure attention, which ensures that our model can capture information and clues both of source tweet content and propagation structure. In addition, the two attention mechanisms in the model can find key clue words in fake news texts and suspicious users in the propagation structure. We conduct experiments on two real-world datasets, and the results demonstrate that MVAN can significantly outperform state-of-the-art methods by 2.5\% in accuracy on average, and produce a reasonable explanation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。