Towards Robust and Explainable Early Rumor Detection: A Noise-Aware Graph Learning Framework
Abstract
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Social media platforms have changed the communication process in such a radical way that now it takes just a few moments for unverified or fake news to reach millions of users. While in the past couple of years various methods have been developed to fight against the spread of false rumors, there are still many challenges to face. These challenges include dealing with noisy data, fast detection, and offering actionable insights for giving quick solutions. The current study aimed to put forward a conceptual noise-aware graph-based framework for the early detection of false rumors on social media. The framework melds together robustness, propagation-aware graph representations, and lightweight early-warning mechanisms in a single design. In order to depict the propagation patterns, the framework makes use of Graph Neural Networks (GNNs) for graph-based propagation modeling and also supports an uncertainty estimation module to handle data biases and incompleteness. One of the main features of the framework is the pinpointing of the most influential posts, users, and propagation paths that have a direct effect on the detection outcomes. While this work is not centered around experimental results, it lays out in detail the real-world validation plan using benchmark datasets, such as Twitter15/16, Weibo, and FakeNewsNet. The integration of noise-awareness, early detection, and interpretability in a single framework is the first step in the direction of robust and explainable rumor detection, thus opening new avenues for future theoretical and experimental work in this field.
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