Modelling Social Context for Fake News Detection: A Graph Neural Network Based Approach

Detection of fake news is crucial to ensure the authenticity of information and maintain the news ecosystem's reliability. Recently, there has been an increase in fake news content due to the recent proliferation of social media and fake content generation techniques such as Deep-Fake. The majority of the existing modalities of fake news detection focus on content-based approaches. However, most of these techniques fail to deal with ultra-realistic synthesized media produced by generative models. Our recent studies find that the propagation characteristics of authentic and fake news are distinguishable, irrespective of their modalities. In this regard, we have investigated the auxiliary information based on social context to detect fake news. This paper has analyzed the social context of fake news detection with a hybrid graph neural network-based approach. This hybrid model is based on integrating a graph neural network on the propagation of news and bi-directional encoder representations from the transformers model on news content to learn the text features. Thus this proposed approach learns the content as well as the context features and hence able to outperform the baseline models with an f1-score of 0.91 on Politifact and 0.93 on the Gossipcop dataset, respectively