{"title":"Rumor Governance Under Uncertain Conditions: An Evolutionary Game Theory Analysis","authors":"Xuefan Dong, Lei Tang","doi":"10.1007/s40745-025-00606-y","DOIUrl":null,"url":null,"abstract":"<div><p>In the rapidly evolving landscape of online information dissemination, managing rumors has become an imperative challenge for governments worldwide. This study employs a tripartite evolutionary game model to examine the behavior evolution of the government, online media, and netizens in the process of rumor propagation under uncertain conditions. The innovation of the model lies in considering the probability of successful rumor detection under government regulation, the uncertainty of rumor dissemination by online media and netizens, and introducing a dynamic government penalty mechanism. Through simulation and analysis, we identify the evolutionarily stable strategies of each participant under different scenarios and provide specific governance strategies for each party involved. The results reveal that appropriate government penalties, proactive regulation by online media, and rational choices by netizens can effectively curb rumor spreading. In uncertain environments, adopting flexible policies and dynamic adjustment mechanisms is crucial for effective rumor governance. The results reveal that appropriate government penalties, proactive regulation by online media, and rational choices by netizens can effectively curb rumor spreading. In uncertain environments, adopting flexible policies and dynamic adjustment mechanisms is crucial for effective rumor governance. This study not only enriches the application of evolutionary game theory but also offers practical strategic recommendations for policymakers to address the challenges of rumor propagation.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"12 3","pages":"1073 - 1111"},"PeriodicalIF":0.0000,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Data Science","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s40745-025-00606-y","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Decision Sciences","Score":null,"Total":0}
引用次数: 0
Abstract
In the rapidly evolving landscape of online information dissemination, managing rumors has become an imperative challenge for governments worldwide. This study employs a tripartite evolutionary game model to examine the behavior evolution of the government, online media, and netizens in the process of rumor propagation under uncertain conditions. The innovation of the model lies in considering the probability of successful rumor detection under government regulation, the uncertainty of rumor dissemination by online media and netizens, and introducing a dynamic government penalty mechanism. Through simulation and analysis, we identify the evolutionarily stable strategies of each participant under different scenarios and provide specific governance strategies for each party involved. The results reveal that appropriate government penalties, proactive regulation by online media, and rational choices by netizens can effectively curb rumor spreading. In uncertain environments, adopting flexible policies and dynamic adjustment mechanisms is crucial for effective rumor governance. The results reveal that appropriate government penalties, proactive regulation by online media, and rational choices by netizens can effectively curb rumor spreading. In uncertain environments, adopting flexible policies and dynamic adjustment mechanisms is crucial for effective rumor governance. This study not only enriches the application of evolutionary game theory but also offers practical strategic recommendations for policymakers to address the challenges of rumor propagation.
期刊介绍:
Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed. ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.