A systematic review on fake news research through the lens of news creation and consumption: Research efforts, challenges, and future directions
PLoS ONE, ISSN: 1932-6203, Vol: 16, Issue: 12 December, Page: e0260080
2021
- 51Citations
- 225Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations51
- Citation Indexes50
- 50
- CrossRef8
- Policy Citations1
- Policy Citation1
- Captures225
- Readers225
- 225
Article Description
Background Although fake news creation and consumption are mutually related and can be changed to one another, our review indicates that a significant amount of research has primarily focused on news creation. To mitigate this research gap, we present a comprehensive survey of fake news research, conducted in the fields of computer and social sciences, through the lens of news creation and consumption with internal and external factors. Methods We collect 2,277 fake news-related literature searching six primary publishers (ACM, IEEE, arXiv, APA, ELSEVIER, and Wiley) from July to September 2020. These articles are screened according to specific inclusion criteria (see Fig 1). Eligible literature are categorized, and temporal trends of fake news research are examined. Results As a way to acquire more comprehensive understandings of fake news and identify effective countermeasures, our review suggests (1) developing a computational model that considers the characteristics of news consumption environments leveraging insights from social science, (2) understanding the diversity of news consumers through mental models, and (3) increasing consumers' awareness of the characteristics and impacts of fake news through the support of transparent information access and education. Conclusion We discuss the importance and direction of supporting one's “digital media literacy” in various news generation and consumption environments through the convergence of computational and social science research.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85121042362&origin=inward; http://dx.doi.org/10.1371/journal.pone.0260080; http://www.ncbi.nlm.nih.gov/pubmed/34882703; https://dx.plos.org/10.1371/journal.pone.0260080; https://dx.doi.org/10.1371/journal.pone.0260080; https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0260080
Public Library of Science (PLoS)
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