Time-Dynamics of (Mis)Information Spread on Social Networks: A COVID-19 Case Study

Zafer Duzen; Mirela Riveni; Mehmet S. Aktas

doi:10.1007/978-3-031-53503-1_13

Time-Dynamics of (Mis)Information Spread on Social Networks: A COVID-19 Case Study

Zafer Duzen^*, Mirela Riveni, Mehmet S. Aktas

^*Corresponding author for this work

Information Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

2 Citations (Scopus)

6 Downloads (Pure)

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.

Original language	English
Title of host publication	Complex Networks and Their Applications XII - Proceedings of The 12th International Conference on Complex Networks and their Applications
Editors	Hocine Cherifi, Luis M. Rocha, Chantal Cherifi, Murat Donduran
Publisher	Springer
Pages	156-167
Number of pages	12
ISBN (Electronic)	978-3-031-53503-1
ISBN (Print)	978-3-031-53502-4
DOIs	https://doi.org/10.1007/978-3-031-53503-1_13
Publication status	Published - 2024
Event	12th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2023 - Menton, France Duration: 28-Nov-2023 → 30-Nov-2023

Publication series

Name	Studies in Computational Intelligence
Volume	1144 SCI
ISSN (Print)	1860-949X
ISSN (Electronic)	1860-9503

Conference

Conference	12th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2023
Country/Territory	France
City	Menton
Period	28/11/2023 → 30/11/2023

Keywords

centrality metrics
large scale networks
misinformation
network science
social networks

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10.1007/978-3-031-53503-1_13

Time-Dynamics of (Mis)Information Spread on Social Networks: A COVID-19 Case StudyFinal publisher's version, 2.02 MBLicence: Taverne

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Cite this

Duzen, Z., Riveni, M., & Aktas, M. S. (2024). Time-Dynamics of (Mis)Information Spread on Social Networks: A COVID-19 Case Study. In H. Cherifi, L. M. Rocha, C. Cherifi, & M. Donduran (Eds.), Complex Networks and Their Applications XII - Proceedings of The 12th International Conference on Complex Networks and their Applications (pp. 156-167). (Studies in Computational Intelligence; Vol. 1144 SCI). Springer. https://doi.org/10.1007/978-3-031-53503-1_13

Duzen, Zafer ; Riveni, Mirela ; Aktas, Mehmet S. / Time-Dynamics of (Mis)Information Spread on Social Networks : A COVID-19 Case Study. Complex Networks and Their Applications XII - Proceedings of The 12th International Conference on Complex Networks and their Applications. editor / Hocine Cherifi ; Luis M. Rocha ; Chantal Cherifi ; Murat Donduran. Springer, 2024. pp. 156-167 (Studies in Computational Intelligence).

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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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author = "Zafer Duzen and Mirela Riveni and Aktas, {Mehmet S.}",

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Duzen, Z, Riveni, M & Aktas, MS 2024, Time-Dynamics of (Mis)Information Spread on Social Networks: A COVID-19 Case Study. in H Cherifi, LM Rocha, C Cherifi & M Donduran (eds), Complex Networks and Their Applications XII - Proceedings of The 12th International Conference on Complex Networks and their Applications. Studies in Computational Intelligence, vol. 1144 SCI, Springer, pp. 156-167, 12th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2023, Menton, France, 28/11/2023. https://doi.org/10.1007/978-3-031-53503-1_13

Time-Dynamics of (Mis)Information Spread on Social Networks: A COVID-19 Case Study. / Duzen, Zafer; Riveni, Mirela; Aktas, Mehmet S.
Complex Networks and Their Applications XII - Proceedings of The 12th International Conference on Complex Networks and their Applications. ed. / Hocine Cherifi; Luis M. Rocha; Chantal Cherifi; Murat Donduran. Springer, 2024. p. 156-167 (Studies in Computational Intelligence; Vol. 1144 SCI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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T2 - 12th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2023

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AU - Riveni, Mirela

AU - Aktas, Mehmet S.

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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 MS. Time-Dynamics of (Mis)Information Spread on Social Networks: A COVID-19 Case Study. In Cherifi H, Rocha LM, Cherifi C, Donduran M, editors, Complex Networks and Their Applications XII - Proceedings of The 12th International Conference on Complex Networks and their Applications. Springer. 2024. p. 156-167. (Studies in Computational Intelligence). doi: 10.1007/978-3-031-53503-1_13