基于SIDR模型的谣言传播与源头检测研究

doi:10.11925/infotech.2096-3467.2020.0715

数据分析与知识发现

2021, Vol. 5

Issue (1): 78-89 https://doi.org/10.11925/infotech.2096-3467.2020.0715

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基于SIDR模型的谣言传播与源头检测研究

陈一新,陈馨悦,刘奕,王汉桢,赖拥庆,徐扬(

)

北京大学信息管理系北京 100871

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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摘要

【目的】 探究谣言传播的特征,识别谣言源头,减小谣言造成的危害。【方法】 在传统传染病模型的基础上,加入“辟谣者”状态,以现实中谣言传播特征为参考设置节点状态转化规则,构建基于社交网络中节点交互作用的SIDR谣言传播模型,并基于该模型提出谣言源头检测算法,利用Beam Search搜索算法进行模型优化。在理论建模的基础上,选取典型的真实谣言案例进行验证与分析。【结果】 SIDR模型能够较准确地刻画现实中的谣言传播事件,源头处辟谣能够抑制谣言传播;本文提出的源头检测算法在谣言传播的初期Top5节点的识别准确率达到83%。【局限】 未考虑现实中社交网络的动态变化,选取实例的代表性有限。【结论】 研究结果可为谣言发展趋势的预测和谣言源头的识别提供指导。

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	陈一新
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关键词 ： SIDR模型, 谣言传播, 辟谣, 源头检测

Abstract：

[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 words： SIDR Model Rumor Propagation Rumor Rebuttal Source Detection

收稿日期: 2020-07-21 出版日期: 2021-02-05

ZTFLH:

G350

通讯作者: 徐扬 E-mail: yang.xu@pku.edu.cn

引用本文:

陈一新,陈馨悦,刘奕,王汉桢,赖拥庆,徐扬. 基于SIDR模型的谣言传播与源头检测研究[J]. 数据分析与知识发现, 2021, 5(1): 78-89.
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.

链接本文:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2020.0715 或 https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2021/V5/I1/78

Table 1 节点状态定义

Fig. 1 SIDR模型状态转规则

参数	含义
$α 1$	S态遇到I态后转化为I态的概率
$α 2$	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
$γ 1$	D态遇到I态后保持D态的概率
$γ 2$	D态遇到D态后保持D态的概率

Table 2 状态转化参数定义

参数事件	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
$α 1$	0.4	0.2
$α 2$	0.3	0.3
$β$	0.4	0.2
$θ$	0.5	0.6
$γ 1$	0.4	0.1
$γ 2$	0.4	0.1

Table 3 事件模拟参数设置

Fig.2 “江苏医疗队物资被扣”事件微博数据与模型模拟对比

Fig.3 “毛领容易粘病毒事件”微博数据与模型模拟对比

Fig.4 源头辟谣效果模拟

Table 4 无标度网络源头检测实验结果

Table 5 Facebook网络源头检测实验结果

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