Rumor Detection Based on SAGNN: Simplified Aggregation Graph Neural Networks

Identifying fake news on media has been an important issue. This is especially true considering the wide spread of rumors popular social networks such as Twitter. Various kinds techniques have proposed for automatic rumor detection. In this work, we study application graph neural classification at a lower level, instead applying existing network architectures to detect rumors. The responses and false display distinct characteristics. suggests that it essential capture interactions in effective manner deep learning achieve better detection performance. To end present simplified aggregation architecture. Experiments publicly available Twitter datasets demonstrate performance par with or even than state-of-the-art convolutional networks, while significantly reducing computational complexity.