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The disaster of misinformation: a review of research in social media.
- Source :
-
International journal of data science and analytics [Int J Data Sci Anal] 2022; Vol. 13 (4), pp. 271-285. Date of Electronic Publication: 2022 Feb 15. - Publication Year :
- 2022
-
Abstract
- The spread of misinformation in social media has become a severe threat to public interests. For example, several incidents of public health concerns arose out of social media misinformation during the COVID-19 pandemic. Against the backdrop of the emerging IS research focus on social media and the impact of misinformation during recent events such as the COVID-19, Australian Bushfire, and the USA elections, we identified disaster, health, and politics as specific domains for a research review on social media misinformation. Following a systematic review process, we chose 28 articles, relevant to the three themes, for synthesis. We discuss the characteristics of misinformation in the three domains, the methodologies that have been used by researchers, and the theories used to study misinformation. We adapt an Antecedents-Misinformation-Outcomes (AMIO) framework for integrating key concepts from prior studies. Based on the AMIO framework, we further discuss the inter-relationships of concepts and the strategies to control the spread of misinformation on social media. Ours is one of the early reviews focusing on social media misinformation research, particularly on three socially sensitive domains; disaster, health, and politics. This review contributes to the emerging body of knowledge in Data Science and social media and informs strategies to combat social media misinformation.<br />Competing Interests: Conflict of interestOn behalf of two authors, the corresponding author states that there is no conflict of interest in this research paper.<br /> (© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022.)
Details
- Language :
- English
- ISSN :
- 2364-415X
- Volume :
- 13
- Issue :
- 4
- Database :
- MEDLINE
- Journal :
- International journal of data science and analytics
- Publication Type :
- Academic Journal
- Accession number :
- 35194559
- Full Text :
- https://doi.org/10.1007/s41060-022-00311-6