Paper Info
Title
The research on propagation modeling and governance strategies of online rumors based on behavior-attitude
Abstract
Purpose The purpose of this paper is to achieve effective governance of online rumors through the proposed rumor propagation model and immunization strategy. Design/methodology/approach The paper leverages the agent-based modeling (ABM) method to model individuals from two aspects, behavior and attitude. Based on the analysis and research of online data, we propose a rumor propagation model, namely the Untouched view transmit removed-Susceptible hesitate agree disagree (Unite-Shad), and devise an immunization strategy, namely the Gravity Immunization Strategy (GIS). A graph-based framework, namely Pregel, is used to carry out the rumor propagation simulation experiments. Through the experiments, the rationality of the Unite-Shad and the effectiveness of the GIS are verified. Findings The study discovers that the inconsistency between human behaviors and attitudes in rumor propagation can be explained by the Unite-shad model. Besides, the GIS, which shows better performance in small-world networks than in scale-free networks, can effectively suppress rumor propagation in the early stage. Research limitations/implications This paper provides an effective immunization strategy for rumor governance. Specifically, the Unite-Shad model reveals the mechanism of rumor propagation, and the GIS provides an effective governance method for selecting immune nodes. Originality/value The inconsistency of human behaviors and attitudes in real scenes is modeled in the Unite-Shad model. Combined with the model, the definition of diffusion domain is proposed and a novel immunization strategy, namely GIS, is designed, which is significant for the social governance of rumor propagation.
Year
DOI
Venue
2022
10.1108/INTR-08-2020-0480
INTERNET RESEARCH
Keywords
DocType
Volume
Rumor propagation, Social governance, Immunization strategy, Agent-based modeling
Journal
32
Issue
ISSN
Citations 
2
1066-2243
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Hailiang Chen100.34
Chuan Ai200.34
Bin Chen33517.45
Zhao Yong49014.85
Kaisheng Lai500.34
Lingnan He634.51
Zhihan Liu700.34