Fake news detection with Graph Neural Networks

The rapid advancement of technology and the accelerating dissemination of information have contributed to a significant increase in the prevalence of fake news, defined as mislead- ing or deliberately deceptive news articles. This proliferation poses a significant challenge, as it can erode trust in institutions and the media, incite violence and hatred, influence elections and public policy, discourage civic engagement, and fracture social cohesion. Therefore, developing e"ective and e!cient strategies for detecting and intercepting on- line fake news is crucial. This thesis makes three research contributions focused on fake news detection, each employing Graph Neural Networks (GNNs) and leveraging diverse graph data features to model social network. The first contribution addresses the evolving relationships between various entity types within a social network over time. We model the social network as a Dynamic Hetero- geneous Information Network (DHIN), where "dynamic" captures temporal changes and "heterogeneous" represents multiple node and edge types. To mitigate the high compu- tational cost typically associated with DHINs, we designed a GNN model incorporating a dynamic update mechanism. This approach significantly reduces the training time re- quired for DHINs by 35% to 45% compared to existing techniques. The second contribution focuses on early-stage fake news detection by examining prop- agation patterns during the initial phases of news dissemination. We developed a GNN architecture that separately trains a homogeneous information network (representing news propagation) and a heterogeneous information network (representing the broader social context). A fusion mechanism integrates the representations from both networks at desig- nated intervals. Findings demonstrate that early-stage propagation patterns o"er strong indicators of a news item’s veracity. Within the initial two-hour period, our model achieves approximately 80% accuracy on real-world datasets. The third contribution investigates the largely unexplored influence of the author-topic relationship on news veracity. We utilize author-topic modelling to establish these re- lationships and represent them using hypergraphs, which capture complex interactions among multiple vertices. Furthermore, by establishing relationships between hyperedges, we model higher-order interactions without introducing additional nodes. On datasets containing author information, our model demonstrates outstanding performance com- pared to existing methods. Consequently, a significant deviation in an author’s observed topic distribution from their expected distribution, as derived from the author-topic rela- tionship, can serve as a strong signal for fake news detection.