The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
A systematic literature review on disinformation: Toward a unified taxonomical framework
Abstract
The scale, volume, and distribution speed of disinformation raise concerns in governments, businesses, and citizens. To respond effectively to this problem, we first need to disambiguate, understand, and clearly define the phenomenon. Our online information landscape is characterized by a variety of different types of false information. There is no commonly agreed typology framework, specific categorization criteria, and explicit definitions as a basis to assist the further investigation of the area. Our work is focused on filling this need. Our contribution is twofold. First, we collect the various implicit and explicit disinformation typologies proposed by scholars. We consolidate the findings following certain design principles to articulate an all-inclusive disinformation typology. Second, we propose three independent dimensions with controlled values per dimension as categorization criteria for all types of disinformation. The taxonomy can promote and support further multidisciplinary research to analyze the special characteristics of the identified disinformation types.
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Cites
The spread of true and false news online
Vosoughi, S., Roy, D., & Aral, S. (2018) · Science · doi:10.1126/science.aap9559 · Cited by 8,847 · cited 1×
“…that are used to refer to false, untrue, or half-true information such as “fake news” (Lazer et al., 2018; Zhou and Zafarani, 2018), “false news” (Vosoughi et al., 2018), “digital misinformation” (World Economic Forum, 2018), “disinformation” (Amazeen and Bucy, 2019; HLEG, 2018; Wardle and…”
Social Media and Fake News in the 2016 Election
Allcott, H., & Gentzkow, M. (2017) · Journal of Economic Perspectives · doi:10.1257/jep.31.2.211 · Cited by 6,767
Twitter mood predicts the stock market
Bollen, J., Mao, H., & Zeng, X. (2011) · Journal of Computational Science · doi:10.1016/j.jocs.2010.12.007 · Cited by 5,206
Lazer, D. M. J., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., Metzger, M. J., Nyhan, B., Pennycook, G., Rothschild, D., et al. (2018) · Science · doi:10.1126/science.aao2998 · Cited by 4,160
Fake News Detection on Social Media: A Data Mining Perspective
Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3137597.3137600 · Cited by 3,304
Defining “Fake News”: A typology of scholarly definitions
Tandoc, E. C., Lim, Z. W., & Ling, R. (2018) · Digital Journalism · doi:10.1080/21670811.2017.1360143 · Cited by 2,172
Pennycook, G., & Rand, D. G. (2019) · Cognition · doi:10.1016/j.cognition.2018.06.011 · Cited by 2,022
Accuracy of Deception Judgments
Bond, C. F., & DePaulo, B. M. (2006) · Personality and Social Psychology Review · doi:10.1207/s15327957pspr1003_2 · Cited by 1,953
Systematic Literature Review on the Spread of Health-related Misinformation on Social Media
Wang, Y., McKee, M., Torbica, A., & Stuckler, D. (2019) · Social Science & Medicine · doi:10.1016/j.socscimed.2019.112552 · Cited by 1,806 · cited 1×
“Disinformation can also have a negative impact in environmental policies; Ward (2018) and Hotten (2015) are typical examples.”
Background"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection
Wang, W. Y. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p17-2067 · Cited by 1,516
Cited by
Fake news, disinformation and misinformation in social media: a review
Aïmeur, E., Amri, S., & Brassard, G. (2023) · Social Network Analysis and Mining · doi:10.1007/s13278-023-01028-5 · Cited by 772
Sun, Y., Sheng, D., Zhou, Z., & Wu, Y. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-03811-x · Cited by 327
In generative AI we trust: can chatbots effectively verify political information?
Kuznetsova, E., Makhortykh, M., Vziatysheva, V., Stolze, M., Baghumyan, A., & Urman, A. (2025) · Journal of Computational Social Science · doi:10.1007/s42001-024-00338-8 · Cited by 32
Harper, T., Tomkinson, S., & Attwell, K. (2022) · Journal of Health Communication · doi:10.1080/10810730.2022.2136307 · Cited by 11
-, A. B. (2024) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2024.v06i04.26309 · Cited by 2
Metadata
- Authors
- Kapantai, E., Christopoulou, A., Berberidis, C., & Peristeras, V.
- Venue
- New Media & Society
- Publisher
- SAGE Publications
- Published
- 2020-09-20
- Volume
- 23
- Issue
- 5
- Pages
- 1301-1326
- ISSN
- 1461-4448 (ISSN-L)
- 1461-7315
- OpenAlex
- W3087305044
- Cited by
- 259
- References
- 56
- FWCI
- 7.19
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
