Network Dynamics of COVID-19 Fake and True News Diffusion Networks

Sumaiyah Zahid, Rauf Ahmed Shams Malick and Muhammad Rabeet Sagri
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Sumaiyah Zahid: School of Computing, National University of Computer and Emerging Sciences, Karachi, Pakistan
Rauf Ahmed Shams Malick: School of Computing, National University of Computer and Emerging Sciences, Karachi, Pakistan
Muhammad Rabeet Sagri: School of Computing, National University of Computer and Emerging Sciences, Karachi, Pakistan

Journal of Information & Knowledge Management (JIKM), 2022, vol. 21, issue Supp01, 1-16

Abstract: Social media platforms have become an integral source to spread and consume information. Twitter has emerged as the fastest medium to disseminate any information. This blind trust on social media has raised the concern to quantify the truth or fakeness of what we are consuming. During COVID-19, the usage of social platforms has dramatically increased in everyone’s life. It is high time to distinguish between the type of users involved in spreading fake and true news content. Our study aims to answer two questions. First, what is the complex network structure of users involved in spreading any news? How two types (i.e. Fake and True) of networks are different in terms of network topology. Second, what is the role of influential users in spreading both types of news? To answer these, the fake and true news of COVID-19 are collected which have been classified by fact-checking websites. Diffusion networks have been created to perform the experiments. Network topological analysis revealed that despite having differences, most properties show similar behaviour. Though, it can be stated that during COVID-19, behaviour of users remained the same in spreading fake or true content. Resilience analysis discovered that fake networks were more densely connected than true ones. There were more centric nodes or influential users were present in Fake news networks than True news networks.

Keywords: COVID-19 news; fake news; true news; twitter; diffusion network; news resilience analysis; social networks (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0219649222400093

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