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Data Analysis and Knowledge Discovery  0, Vol. Issue (): 1-    DOI: 10.11925/infotech.2020.0715
Current Issue | Archive | Adv Search |
Research on rumor propagation and source detection based on SIDR model
CHEN Yixin,CHEN Xinyue,LIU Yi,WANG Hanzhen,LAI Yongqing,XU Yang
(Department of Information Management, Peking University, 100871 Beijing)
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[Objective] In order to explore the characteristics of rumor propagation, detect the source of rumor and reduce the harm caused by rumor.

[Methods] This paper adds the state of “rumor denier” to the traditional infectious disease model, sets the node state transformation rules according to the characteristics of rumor propagation in reality, and constructs a SIDR rumor propagation model considering the node interaction in social networks. Based on SIDR model, this paper proposes a rumor source detection algorithm and uses Beam search algorithm to optimize the detection model. On the basis of theoretical modeling, this paper selects typical real-world rumor cases to verify the model.

[Results] SIDR model can accurately simulate the propagation of rumors; rebutting humor from the source can inhibit the spread of rumor, and our source detection algorithm achieve high accuracy when applied in the early stage of rumor propagation.

[Limitations] This paper do not consider the dynamic changes of social networks in reality, and the representativeness of selected cases is limited. [Conclusions] The results provide guidance for the prediction of rumor propagation and the identification of rumor source.

Key words SIDR Model      rumor propagation      rumor rebuttal      source detection      
Published: 09 September 2020
ZTFLH:  G350  

Cite this article:

CHEN Yixin, CHEN Xinyue, LIU Yi, WANG Hanzhen, LAI Yongqing, XU Yang. Research on rumor propagation and source detection based on SIDR model . Data Analysis and Knowledge Discovery, 0, (): 1-.

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[1] Chen Yixin,Chen Xinyue,Liu Yi,Wang Hanzhen,Lai Yongqing,Xu Yang. Detecting Rumor Dissemination and Sources with SIDR Model[J]. 数据分析与知识发现, 2021, 5(1): 78-89.
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