ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2021, Vol. 58 ›› Issue (7): 1395-1411.doi: 10.7544/issn1000-1239.2021.20200810

Special Issue: 2021虚假信息检测专题

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A Rumor Detection Approach Based on Multi-Relational Propagation Tree

Hu Dou1, Wei Lingwei2,3, Zhou Wei2, Huai Xiaoyong1, Han Jizhong2, Hu Songlin2,3   

  1. 1(National Computer System Engineering Research Institute of China, Beijing 100083);2(Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093);3(School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100049)
  • Online:2021-07-01
  • Supported by: 
    This work was supported by the National Key Research and Development Program of China (2018YFC0806900).

Abstract: Rumor detection has aroused increasing attention due to the damage caused by rampant rumors. Recent studies leverage the post content and propagation structure to detect rumors based on deep-learning based models. However, most of them focus on explicit interactions between posts in the spreading process, while ignoring the modeling of implicit relations, which limits the ability to encode the propagation structure. For example, in the interactive pattern of forwarding (commenting), there are often local implicit interactions between multiple forwarded (commented) posts. In this paper, we propose a rumor detection approach based on a multi-relational propagation tree, investigating multiple kinds of dependencies between posts and enhancing the influence of important posts, to capture richer propagation patterns. Specifically, we formulate the textual content and the propagation tree structure as a heterogeneous graph. Then, we present a novel multi-relational graph convolutional network to learn the inter-level dependency between parent and child nodes and the intra-level dependency between sibling nodes. Meanwhile, we exploit the source post and key spreading posts to model the potential influence of important posts in the spreading process. Finally, we aggregate the node features to learn a more discriminative representation for rumor detection. Extensive experiments on three public benchmark datasets demonstrate the effectiveness and superiority of our proposed method.

Key words: rumor detection, propagation tree, graph convolutional network, information propagation, social media

CLC Number: