Modelling Context and Content Features for Fake News Detection

With the emergence and rapid development of social networks, an increasing amount of news has been spreading. In addition to the benefits of factual information, there are always risks associated with the dissemination of fake news and preventing the spread of fake news has been a concern for researchers. Many methods have been proposed to detect fake news, but they do not fully extract important information related to news content and context, and rarely consider modelling the simultaneous exploitation of the news context and content in fake news detection. This study proposes a method to improve the performance of fake news detection by modelling features related to news context and content. First, we combine contextualised embeddings (e.g., BERT) and dependency-based embeddings (e.g., dependency-based GCN) to enhance the performance of the content representations of news and reviews posting them. Second, we combine all available review texts related to news belonging to the user. Third, we explore all the reviews that other users had posted about current news by clearly creating review representations posted by the same user about the same news. This leads the model to quickly memorise all reviews related to news from one user. Finally, we model the news content features and the modelled news context features to enhance the richness of the news feature representations. Experimental results on the PolitiFact and GossipCop datasets show improvement to the state-of-the-art method of more than three percentage points in the best case.