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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (1): 78-89    DOI: 10.11925/infotech.2096-3467.2020.0715
Current Issue | Archive | Adv Search |
Detecting Rumor Dissemination and Sources with SIDR Model
Chen Yixin,Chen Xinyue,Liu Yi,Wang Hanzhen,Lai Yongqing,Xu Yang()
Department of Information Management, Peking University, Beijing 100871, China
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[Objective] This paper explores the characteristics of rumor sources and dissemination patterns, aiming to reduce their negative effects. [Methods] First, we added “fact checkers” to the traditional infectious disease model, and set changing rules for node status based on the characteristics of rumor dissemination. Then, we constructed a SIDR model with the node interaction in social networks. Third, we proposed an algorithm based on SIDR model to detect rumor sources. Finally, we optimized the proposed model with the Beam search algorithm. [Results] We examined the new model with real-world cases and found it accurately simulated the propagation of rumors. Identifying rumor sources could constrain their spread. The accuracy of our algorithm was up to 83% at the early stage.[Limitations] This paper does not consider the dynamic changes of social networks, and more representative cases should be included. [Conclusions] The proposed model could help us identify rumor sources and predict their development.

Key wordsSIDR Model      Rumor Propagation      Rumor Rebuttal      Source Detection     
Received: 21 July 2020      Published: 05 February 2021
ZTFLH:  G350  
Corresponding Authors: Xu Yang     E-mail:

Cite this article:

Chen Yixin,Chen Xinyue,Liu Yi,Wang Hanzhen,Lai Yongqing,Xu Yang. Detecting Rumor Dissemination and Sources with SIDR Model. Data Analysis and Knowledge Discovery, 2021, 5(1): 78-89.

URL:     OR

名称 简称 含义
未知状态(Susceptible) S 未接触过谣言的初始状态
感染状态(Infected) I 已接触谣言,相信该谣言并传播谣言的状态
辟谣状态(Denied) D 已接触谣言,不相信该谣言并传播辟谣信息的状态
退出状态(Removal) R 由于失去兴趣或遗忘等原因,不参与该谣言或辟谣信息的传播
Definition of Node State
SIDR Model State Transition Rules
参数 含义
α1 S态遇到I态后转化为I态的概率
α2 S态遇到I态后转化为D态的概率
β I态遇到I态后保持I态的概率
θ I态遇到D态后转化为D态的概率
γ1 D态遇到I态后保持D态的概率
γ2 D态遇到D态后保持D态的概率
Definition of State Transition Parameters

江苏医疗队物资被扣 毛领容易粘病毒
t(“潜在辟谣者”占比) 0.2 0.2
α1 0.4 0.2
α2 0.3 0.3
β 0.4 0.2
θ 0.5 0.6
γ1 0.4 0.1
γ2 0.4 0.1
Parameter Settings of Event Simulation
“Jiangsu Medical Team Supply Detainment” Event-Comparison of Microblog Data and Model Simulation
“Fur Collars Can Stick to Viruses” Event-Comparison of Microblog Data and Model Simulation
Simulation Effect of Refuting Rumors from the Source

Top1 Top3 Top5
T0/3 47% 74% 83%
2T0/3 42% 65% 73%
T0 35% 58% 66%
3 T0/2 26% 41% 50%
2 T0 19% 38% 50%
Experimental Results of Source Detection in Scale-Free Network

Top1 Top3 Top5
T0/3 45% 71% 82%
2T0/3 41% 62% 69%
T0 36% 57% 61%
3 T0/2 25% 43% 48%
2 T0 20% 37% 45%
Experimental Results of Source Detection in Facebook Network
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