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Intervention strategies for misinformation sharing on social media: A bibliometric analysis

Zainudin, Juanita, Ali, Nazlena Mohamad, Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389 and Ijab, Mohamad Taha (2024) Intervention strategies for misinformation sharing on social media: A bibliometric analysis. IEEE Access . pp. 1-22. ISSN 2169-3536

Abstract
Widely distributed misinformation shared across social media channels is a pressing issue that poses a significant threat to many aspects of society’s well-being. Inaccurate shared information causes confusion, can adversely affect mental health, and can lead to mis-informed decision-making. Therefore, it is important to implement proactive measures to intervene and curb the spread of misinformation where possible. This has prompted scholars to investigate a variety of intervention strategies for misinformation sharing on social media. This study explores the typology of intervention strategies for addressing misinformation sharing on social media, identifying 4 important clusters – cognition-based, automated-based, information-based, and hybrid-based. The literature selection process utilized the PRISMA method to ensure a systematic and comprehensive analysis of relevant literature while maintaining transparency and reproducibility. A total of 139 articles published from 2013-2023 were then analyzed. Meanwhile, bibliometric analyses were conducted using performance analysis and science mapping techniques for the typology development. A comparative analysis of the typology was conducted to reveal patterns and evolution in the field. This provides valuable insights for both theory and practical applications. Overall, the study concludes that scholarly contributions to scientific research and publication help to address research gaps and expand knowledge in this field. Understanding the evolution of intervention strategies for misinformation sharing on social media can support future research that contributes to the development of more effective and sustainable solutions to this persistent problem.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:Bibliometric analysis, intervention strategy, misinformation, sharing behavior, social media.
Subjects:Computer Science > Artificial intelligence
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > INSIGHT Centre for Data Analytics
Publisher:IEEE
Official URL:https://ieeexplore.ieee.org/document/10697164
Copyright Information:Authors
ID Code:30345
Deposited On:01 Oct 2024 13:01 by Alan Smeaton . Last Modified 01 Oct 2024 13:03
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