Rumor detection based on propagation graph neural network with attention mechanism. (15th November 2020)

• Record Type: Journal Article
• Authors: Wu, Zhiyuan; Pi, Dechang; Chen, Junfu; Xie, Meng; Cao, Jianjun
• Is Part Of: Expert systems with applications. Volume 158(2020)
• Publication Date: 2020-11-15
• Subjects: Rumor detection -- Social network -- Graph neural network -- Social security -- Representation learning; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.113595
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A novel way is proposed to explicitly construct the propagation graph of rumors. A representation learning algorithm based on gated graph neural network is proposed. Two rumor detection models with different classification strategies are proposed. Attention mechanism is included to improve the detection performance. Abstract: Rumors on social media have always been an important issue that seriously endangers social security. Researches on timely and effective detection of rumors have aroused lots of interest in both academia and industry. At present, most existing methods identify rumors based solely on the linguistic information without considering the temporal dynamics and propagation patterns. In this work, we aim to solve rumor detection task under the framework of representation learning. We first propose a novel way to construct the propagation graph by following the propagation structure (who replies to whom) of posts on Twitter. Then we propose a gated graph neural network based algorithm called PGNN, which can generate powerful representations for each node in the propagation graph. The proposed PGNN algorithm repeatedly updates node representations by exchanging information between the neighbor nodes via relation paths within a limited time steps. On this basis, we propose two models, namely GLO-PGNN (rumor detection model based on the global embedding with propagation graph neural network ) and ENS-PGNN (rumor detection model based on the ensemble learning with propagation graph neural network ). They respectively adopt different classification strategies for rumor detection task, and further improve the performance by including attention mechanism to dynamically adjust the weight of each node in the propagation graph. Experiments on a real-world Twitter dataset demonstrate that our proposed models achieve much better performance than state-of-the-art methods both on the rumor detection task and early detection task.