Online information disorder: fake news, bots and trolls
- PMID: 35574264
- PMCID: PMC9080975
- DOI: 10.1007/s41060-022-00325-0
Online information disorder: fake news, bots and trolls
Abstract
Recent years have seen a tremendous increase in the propagation of different types of misinformation and disinformation, including among others fake news, rumours, clickbait and conspiracy theories. Misinformation involved in satire and clickbait, among others, has a different intention from disinformation. Despite many attempts by the research community, the development of technology to assist experts in detecting disinformation remains an open problem due to a number of challenges. For example, there are various biases such as confirmation bias and peer pressure that hinder users from recognizing non-credible information. In addition, fake news is intentionally written to confuse the readers, often containing a mixture of false and real information. To this end, in this editorial we present current challenges in the area of fake news identification and discuss contributions published in our special issue.
Keywords: Fake news; Fake news spreaders; Information disorder.
© The Author(s) 2022.
References
-
- Allcott H, Gentzkow M. Social media and fake news in the 2016 election. J. econ. perspect. 2017;31(2):211–36. doi: 10.1257/jep.31.2.211. - DOI
-
- Bastos MT, Mercea D. The brexit botnet and user-generated hyperpartisan news. Soc. Sci. comp. Rev. 2019;37(1):38–54. doi: 10.1177/0894439317734157. - DOI
-
- Farajtabar, M., Yang, J., Ye, X., et al.: Fake news mitigation via point process based intervention. In: International Conference on Machine Learning, PMLR, pp 1097–1106 (2017)
-
- Gagiano, R., Kim, M.M.H., Zhang, X.J., et al.: Robustness analysis of grover for machine-generated news detection. In: Proceedings of the The 19th Annual Workshop of the Australasian Language Technology Association, pp 119–127 (2021)
Publication types
LinkOut - more resources
Full Text Sources