  Fake news is fabricated information that is presented as genuine, with
intention to deceive the reader. Recently, the magnitude of people relying on
social media for news consumption has increased significantly. Owing to this
rapid increase, the adverse effects of misinformation affect a wider audience.
On account of the increased vulnerability of people to such deceptive fake
news, a reliable technique to detect misinformation at its early stages is
imperative. Hence, the authors propose a novel graph-based framework SOcial
graph with Multi-head attention and Publisher information and news Statistics
Network (SOMPS-Net) comprising of two components - Social Interaction Graph
(SIG) and Publisher and News Statistics (PNS). The posited model is
experimented on the HealthStory dataset and generalizes across diverse medical
topics including Cancer, Alzheimer's, Obstetrics, and Nutrition. SOMPS-Net
significantly outperformed other state-of-the-art graph-based models
experimented on HealthStory by 17.1%. Further, experiments on early detection
demonstrated that SOMPS-Net predicted fake news articles with 79% certainty
within just 8 hours of its broadcast. Thus the contributions of this work lay
down the foundation for capturing fake health news across multiple medical
topics at its early stages.
