Neural network approaches for rumor stance detection: Simulating complex rumor propagation systems

• Published in: Concurrency and computation 2024-07, Vol.36 (16), p.n/a
• Main Authors: Li, Hao, Yang, Wu, Wang, Wei, Wang, Huanran
• Format: Article
• Language: English
• Subjects: Complex systems; dialogue sequence; hierarchical attention mechanism; Human communication; Information flow; Modelling; Neural networks; Propagation; rumor propagation modeling; rumor stance detection; social media; Social networks
• ISSN: 1532-0626; 1532-0634
• DOI: 10.1002/cpe.8093

Summary This research introduces a comprehensive suite of neural network models designed to tackle the challenging task of rumor stance detection within the framework of simulating complex rumor propagation systems. Our objective centers on accurately modeling the intricate structures of rumor dialogues and propagation patterns to identify user stances—whether they are in support, denial, questioning, or commenting on rumors. Unlike conventional methods that rely on simplistic keyword targeting and fail in the nuanced context of social networks, our models delve into the complexities of dialogue and propagation structures, offering a more precise and insightful analysis of rumor dynamics. In addressing the simulation and modeling of complex systems, our approach specifically focuses on the elaborate interaction networks that underpin rumor spread and reception. While our methodology does not directly engage with brain‐like computing paradigms, it reflects a similar level of sophistication in handling layered and complex information flows, analogous to cognitive processes in understanding and interpreting human communications. Employing a hierarchical attention mechanism, our models adeptly parse through multitiered dialogue sequences, effectively distinguishing between various indicators of user stances. This allows for a nuanced and detailed representation of the rumor ecosystem, significantly enhancing the accuracy of stance detection. Through rigorous testing on diverse datasets, our approach has demonstrated superior performance over existing models, thereby establishing a new benchmark in the field.