Mtas: A temporal-aware multiview graph attention framework for early rumor detection on social media

Project Overview & Abstract

The rapid growth of information dissemination on social networks such as Facebook and Twitter, along with the widespread of mobile devices, has intensified the need for early yet accurate rumor detection methods. While existing hybrid machine learning models integrating natural language processing (NLP) and graph neural networks (GNNs) show promising results but often lack effective integration of diverse features and temporal modeling. To overcome these limitations, we introduce MTAS, a novel framework designed to detect rumors effectively by generating a multiview of social data encompassing semantic, inner, global, and temporal features. Specifically, MTAS models social network data into a comprehensive graph structure representing the global relationship among all tweets, words, and users. Through this structure, the framework processes and analyzes propagation patterns, semantic content, and …