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A Differential Epidemic Model for Information, Misinformation, and Disinformation in Online Social Networks: COVID-19 Vaccination

Nitesh Narayan, Rishi Kumar Jha, Anshuman Singh

Source Title: International Journal on Semantic Web and Information Systems (IJSWIS)18(1)

ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781799893967|DOI: 10.4018/IJSWIS.300827

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MLA

Narayan, Nitesh, et al. "A Differential Epidemic Model for Information, Misinformation, and Disinformation in Online Social Networks: COVID-19 Vaccination." IJSWIS vol.18, no.1 2022: pp.1-20. http://doi.org/10.4018/IJSWIS.300827

APA

Narayan, N., Jha, R. K., & Singh, A. (2022). A Differential Epidemic Model for Information, Misinformation, and Disinformation in Online Social Networks: COVID-19 Vaccination. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-20. http://doi.org/10.4018/IJSWIS.300827

Chicago

Narayan, Nitesh, Rishi Kumar Jha, and Anshuman Singh. "A Differential Epidemic Model for Information, Misinformation, and Disinformation in Online Social Networks: COVID-19 Vaccination," International Journal on Semantic Web and Information Systems (IJSWIS) 18, no.1: 1-20. http://doi.org/10.4018/IJSWIS.300827

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Abstract

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 networks have become an important part of our lives, 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 the theoretical results. Data available on Twitter related to COVID-19 vaccination is used to perform the experiment. Finally, the authors discuss the control strategy to minimize the misinformation and disinformation related to vaccination.