在线社交网络中信息、错误信息和虚假信息的差异流行模型

IF 4.1 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal on Semantic Web and Information Systems Pub Date : 2022-01-01 DOI:10.4018/ijswis.300827

N. Narayan, R. Jha, A. Singh

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引用次数: 4

摘要

如今，这个在线社交网络已经成为一个巨大的数据来源。人们在这些平台上积极地分享信息。在线社交网络上的数据可以是错误信息、信息和虚假信息。因为在线社交网络已经成为我们生活的重要组成部分，所以在线社交网络上的信息对我们产生了很大的影响。本文提出了在线社交网络上信息、错误信息和虚假信息的差异流行模型。提出了基本繁殖数的表达式。再次，讨论了系统在无感染平衡点和地方病平衡点的稳定性条件。通过数值模拟验证了理论结果。同样，在推特上与COVID-19疫苗接种相关的数据的帮助下，进行了实验。最后，讨论了控制策略，以尽量减少与疫苗接种有关的错误信息和虚假信息。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A Differential Epidemic Model for Information, Misinformation and Disinformation in Online Social Networks

These days the online social network has become a huge source of data. People are actively sharing information on these platforms. The data on online social networks can be misinformation, information, and disinformation. Because online social network has become an important part of our life, so the information on online social networks makes a great impact on us. Here a differential epidemic model for information, misinformation, and disinformation on online social networks is proposed. The expression for basic reproduction number has been developed. Again, the stability condition for the system at both infection-free and endemic equilibriums points has been discussed. The Numerical simulation has been performed to validate our theoretical results. Again, with the help of data available on twitter related to COVID-19 vaccination is used to perform the experiment. Finally, discuss about the control strategy to minimize the misinformation and disinformation related to vaccination.

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来源期刊

International Journal on Semantic Web and Information Systems 工程技术-计算机：人工智能

CiteScore

6.20

自引率

12.50%

发文量

审稿时长

20 months

期刊介绍： The International Journal on Semantic Web and Information Systems (IJSWIS) promotes a knowledge transfer channel where academics, practitioners, and researchers can discuss, analyze, criticize, synthesize, communicate, elaborate, and simplify the more-than-promising technology of the semantic Web in the context of information systems. The journal aims to establish value-adding knowledge transfer and personal development channels in three distinctive areas: academia, industry, and government.