Abstract
In our study, we investigate the persistence of misinformation in social networks, focusing on the longevity of discussions related to misinformation. We employ the CoVaxxy dataset, which encompasses COVID-19 vaccine-related tweets, and classify tweets as reliable/unreliable based on non-credible sources/accounts. We construct separate networks for retweets, replies, and mentions, applying centrality metrics (degree, betweenness, closeness) to assess tweet significance. Our objective is to determine how long tweets associated with non-credible sources remain active. Our findings reveal a noteworthy correlation: tweets with longer lifespans tend to be influential nodes within the network, while shorter-lived tweets have less impact. y shedding light on the longevity of misinformation within social networks, our research contributes to a better understanding of misinformation propagation dynamics. These insights can inform strategies to combat misinformation during public health crises like the COVID-19 pandemic.
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Duzen, Z., Riveni, M., Aktas, M.S. (2024). Time-Dynamics of (Mis)Information Spread on Social Networks: A COVID-19 Case Study. In: Cherifi, H., Rocha, L.M., Cherifi, C., Donduran, M. (eds) Complex Networks & Their Applications XII. COMPLEX NETWORKS 2023. Studies in Computational Intelligence, vol 1144. Springer, Cham. https://doi.org/10.1007/978-3-031-53503-1_13
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