Identification of core rumor spreaders in online social networks based on multi-stage deep model

doi:10.7523/j.ucas.2022.057

Abstract

Abstract: Online social networks have become the disaster areas where rumors grow. It is of great significance to identify core rumor spreaders for rumor prevention and control. The traditional rumor control model is mainly based on the dynamics of rumor propagation, and it is mainly focused on in-event or post-event control. In view of the timeliness of rumor control, this paper proposes a multi-stage graph convolutional network based on multi-dimensional features (MSF-GCN) deep learning model to accurately locate core rumor spreaders as early as possible and block rumor diffusion from the source. This work compares the MSF-GCN method with other three baseline methods on rumor data set, and the experimental results verify that our method is more efficient.

Key words: online social network, rumor, identify core nodes, GCN

CLC Number:

TP181

LI Yuan, ZHANG Qi, ZHU Jianming, JIAO Jianbin. Identification of core rumor spreaders in online social networks based on multi-stage deep model[J]. Journal of University of Chinese Academy of Sciences, 2024, 41(1): 136-144.

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