Modeling rumor propagation and mitigation across multiple social networks

Chenxu Wang, Gaoshuai Wang, X. Luo, Hui Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)

Abstract

Social media has become an essential part of people's lives with the rapid development of the Internet and mobile technologies. Although social media has brought great convenience for people to communicate, it also powers misinformation such as rumors to reach more people in a short time than ever before. Moreover, social networks are interconnected with each other since users may join multiple social media simultaneously, which makes the situation even worse. Therefore, it is urgent to model the propagation of rumors across multiple social media and investigate mitigation strategies. In this paper, we propose an improved energy model to characterize the propagation of rumors on social networks quantitatively. We also introduce negative energy to simulate the mitigation process of rumors. Finally, we apply our proposed models to study the impact of the linkage rates on rumor propagation across multiple social networks. We conduct extensive experiments based on real social networks to evaluate the impacts of model parameters, network structures, and effective linkage rates. The experimental results demonstrate the effectiveness of our model in characterizing rumor propagation on multiple social networks. We also obtain valuable findings for effective rumor mitigation.

Original languageEnglish
Article number122240
Pages (from-to)1-18
JournalPhysica A: Statistical Mechanics and its Applications
Volume535
DOIs
Publication statusPublished - 1 Dec 2019

Keywords

  • Energy model
  • Multilayer networks
  • Negative energy
  • Rumor mitigation
  • Rumor propagation

ASJC Scopus subject areas

  • Statistics and Probability
  • Condensed Matter Physics

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