论文标题
通过图评论的假新闻检测高级学习
Fake News Detection through Graph Comment Advanced Learning
论文作者
论文摘要
长期以来，虚假信息一直被视为一个严重的社会问题，假新闻是最具代表性的问题之一。更糟糕的是，当今高度发达的社交媒体使假新闻以惊人的速度广泛传播，对人类生活的各个方面造成了重大伤害。但是，社交媒体的受欢迎程度还提供了更好地检测假新闻的机会。与传统的手段不同，仅关注内容或用户评论，有效地协作了异质的社交媒体信息，包括新闻的内容和上下文因素，用户评论以及社交媒体与用户的参与，希望能够更好地发现假新闻。 在本文中提出了以上观察的激励，是一个新颖的检测框架，即图形评论 - 用户高级学习框架（GCAL）。用户评估信息至关重要，但在假新闻检测中没有很好地研究。因此，我们通过基于异质图神经网络的网络表示学习来对用户概念上下文进行建模。我们对两个现实世界数据集进行了实验，这表明所提出的联合模型的表现优于8种伪造新闻检测的最先进的基线方法（准确性至少4％，召回率为7％，F1中的5％）。此外，提出的方法也可以解释。
Disinformation has long been regarded as a severe social problem, where fake news is one of the most representative issues. What is worse, today's highly developed social media makes fake news widely spread at incredible speed, bringing in substantial harm to various aspects of human life. Yet, the popularity of social media also provides opportunities to better detect fake news. Unlike conventional means which merely focus on either content or user comments, effective collaboration of heterogeneous social media information, including content and context factors of news, users' comments and the engagement of social media with users, will hopefully give rise to better detection of fake news. Motivated by the above observations, a novel detection framework, namely graph comment-user advanced learning framework (GCAL) is proposed in this paper. User-comment information is crucial but not well studied in fake news detection. Thus, we model user-comment context through network representation learning based on heterogeneous graph neural network. We conduct experiments on two real-world datasets, which demonstrate that the proposed joint model outperforms 8 state-of-the-art baseline methods for fake news detection (at least 4% in Accuracy, 7% in Recall and 5% in F1). Moreover, the proposed method is also explainable.
