Intelligent Fake News Detection for Consumer QoE via Semantic Spatiotemporal Fusion

In online social media, various fake news highly impact experience quality (QoE) of consumers. Nowadays, researchers in this area concentrated on either semantic analysis or contextual awareness. However, social media is a spatiotemporal place, and much important information such as social activities, social connection and social attributes, can make sense. Hence, this work proposes an intelligent fake news detection method by leveraging semantic spatiotemporal fusion. It uses the RoBERTa model to process posts made by users to understand their semantic content. Then, the processed data is fed into a Long Short-Term Memory network (LSTM) to capture the temporal features of the posts. Additionally, the study constructs a spatial model using Graph Attention Networks (GATs) to simulate the spread of fake news. Performance of the proposed method is testified on a realworld dataset specifically collected by our research team. And the results reveal that this innovative approach not only improves the accuracy of fake news detection but also optimizes consumer service quality.