A comprehensive survey of fake news in social networks: Attributes, features, and detection approaches
Journal of King Saud University - Computer and Information Sciences, ISSN: 1319-1578, Vol: 35, Issue: 6, Page: 101571
2023
- 43Citations
- 193Captures
- 1Mentions
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.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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.
Most Recent News
Study Data from VIT-AP University Update Knowledge of Computer Science (A comprehensive survey of fake news in social networks: Attributes, features, and detection approaches)
2023 JUN 12 (NewsRx) -- By a News Reporter-Staff News Editor at Computer News Today -- Researchers detail new data in computer science. According to
Review Description
The explosion of online social networks in recent decades has significantly improved in which the way individuals communicate with one another. People trust social networks bluntly without knowing the origin and genuinity of the information passed through these networks. Sometimes, unreliable information on online social networks misleads the viewers, and it brings unremovable stains to humanity. Online social networks transform even the original information of the government, which create confusion among the people and people loses confidence over the government. Various types of research have been conducted to identify fake news with high efficiency. In this survey, we describe the basic theories of fake news, investigate and analyze the perspective on fake news, attribute misleading information, an in-depth analysis of disinformation, and methods that have been established for detection. To our knowledge, this research article will assist in facilitating collaborative activities among technical experts, political campaigns, online purchases, and other disciplines that are being used to investigate fake messages.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S1319157823001258; http://dx.doi.org/10.1016/j.jksuci.2023.101571; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85159602361&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S1319157823001258; https://dx.doi.org/10.1016/j.jksuci.2023.101571
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know