[论文解读] FAKEDETECTOR: Effective Fake News Detection with Deep Diffusive Neural Network

Jiawei Zhang, Bowen Dong+1 more|arXiv (Cornell University)|May 22, 2018

Misinformation and Its Impacts参考文献 68被引用 52

一句话总结

本文提出 FakeDetector，是一个具有混合特征学习单元的深度扩散网络，利用文本和关系信号在新闻增强的异构社交网络中联合推断假新闻的可信度，针对文章、创作者和主题。

ABSTRACT

In recent years, due to the booming development of online social networks, fake news for various commercial and political purposes has been appearing in large numbers and widespread in the online world. With deceptive words, online social network users can get infected by these online fake news easily, which has brought about tremendous effects on the offline society already. An important goal in improving the trustworthiness of information in online social networks is to identify the fake news timely. This paper aims at investigating the principles, methodologies and algorithms for detecting fake news articles, creators and subjects from online social networks and evaluating the corresponding performance. This paper addresses the challenges introduced by the unknown characteristics of fake news and diverse connections among news articles, creators and subjects. This paper introduces a novel automatic fake news credibility inference model, namely FAKEDETECTOR. Based on a set of explicit and latent features extracted from the textual information, FAKEDETECTOR builds a deep diffusive network model to learn the representations of news articles, creators and subjects simultaneously. Extensive experiments have been done on a real-world fake news dataset to compare FAKEDETECTOR with several state-of-the-art models, and the experimental results have demonstrated the effectiveness of the proposed model.

研究动机与目标

• 解决在线社交网络中具有未知特征和多样化文章-创作者-主题连接的假新闻检测问题。
• 通过利用文本信号和网络关系，联合推断新闻文章、创作者和主题的可信度分数。
• 开发一种通过新颖的扩散架构融合异质信息的深度学习模型。

提出的方法

• 引入混合特征学习单元 (HFLU) 以提取明确文本特征和通过对文章、创作者和主题文本的 RNN (GRU) 获取的潜在特征。
• 提出一个带门控扩散单元（GDU）的大型扩散网络，以在文章、创作者和主题之间融合信息。
• 定义端到端目标函数，对文章、创作者和主题采用交叉熵损失并附带正则化项。
• 在 PolitiFact 数据上通过反向传播训练模型，并预测所有节点类型的可信度标签。

实验结果

研究问题

• RQ1 明确和潜在的来自文章、创作者和主题的文本特征如何提升假新闻可信度推断？
• RQ2 深度扩散网络是否能有效融合异质关系（署名关系和文章-主题链接）以在文章、创作者和主题上提高检测性能？
• RQ3 联合建模文章、创作者和主题对 PolitiFact 数据中的可信度预测有何影响？

主要发现

• 该框架在真实世界数据上对新闻文章、创作者和主题的可信度推断提供了统一模型。
• PolitiFact 数据集包含 14,055 篇文章、3,634 位创作者和 152 个主题，具有 48,756 条文章-主题链接和 14,055 条创作者-文章链接。
• 带门控扩散单元的深度扩散网络利用显式词特征和潜在文本表示。
• 该模型对主题输入使用遗忘门，对创作者输入使用调整门，以管理跨类型信息流。