Fake News Detection with Context Awareness of the Publisher

The spread of fake news is a significant social problem that can have disastrous impacts on various domains, such as politics and the economy.Therefore, detecting fake news has become a major concern.However, prior research has relied solely on news text to derive news representation, which is inadequate because different news items under the same publisher are interconnected.To address this limitation, we propose an innovative approach called the Publisher-oriented Multi-view Graph Model (PMGM) that leverages the context awareness of the publisher to detect fake news.Our approach enriches the news representation by incorporating publisher profiles and text style features extracted from the news.Specifically, we construct a multi-view graph that encodes various relationships between news items from the same publisher, such as news topics and occasions in which they were released.Furthermore, we leverage a multi-layer Graph Convolutional Network in conjunction with jumping knowledge networks to model the multi-view graph and produce a publisher-oriented contextualized representation of news.Experimental results on two widely used fake news datasets, namely LIAR and Weibo21, demonstrate the effectiveness of our approach.Specifically, the PMGM model outperforms the state-ofthe-art methods significantly.Overall, our proposed model unifies various heterogeneous features and information related to news based on a publisher-oriented approach, thereby offering a novel idea to enhance fake news detection.