Deep Diffusive Neural Network based Fake News Detection from Heterogeneous Social Networks
Deep Diffusive Neural Network based Fake News Detection from Heterogeneous Social Networks
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DOI：
10.1109/bigdata47090.2019.9005556
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
Jiawei Zhang;Bowen Dong;Philip S. Yu
中科院分区：
文献类型：
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作者：
Jiawei Zhang;Bowen Dong;Philip S. Yu
近年来，由于在线社交网络的蓬勃发展，各种商业和政治目的的假新闻在网络世界大量出现并普遍存在。随着欺骗性话语的出现，在线社交网络用户很容易被这些在线虚假新闻感染，这已经给线下社会带来了巨大的影响。提高在线社交网络中信息可信度的一个重要目标是及时识别虚假新闻。本文旨在研究从在线社交网络中检测虚假新闻文章、创建者和主题的原理、方法和算法，并评估相应的性能。本文论述了假新闻的未知特征以及新闻文章、创作者和主题之间的不同联系所带来的挑战。介绍了一种新的假新闻可信度自动推理模型FakeDetector。FakeDetector基于从文本信息中提取的一组显性和隐性特征，建立了一个深度扩散网络模型，以同时学习新闻文章、创建者和主题的表征。在一个真实的假新闻数据集上进行了大量的实验，将FakeDetector与几种最先进的模型进行了比较，实验结果证明了该模型的有效性。
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.