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Cited by 198Misinformation and Its ImpactsSpam and Phishing DetectionComplex Network Analysis Techniques

Combating disinformation in a social media age

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Shu, Kai, Bhattacharjee, Amrita, Alatawi, Faisal, Nazer, Tahora H., Ding, Kaize, Karami, Mansooreh, Liu, Huan·2020·WIREs Data Mining and Knowledge Discovery
DOI 10.1002/widm.1385
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Abstract

The creation, dissemination, and consumption of disinformation and fabricated content on social media is a growing concern, especially with the ease of access to such sources, and the lack of awareness of the existence of such false information. In this article, we present an overview of the techniques explored to date for the combating of disinformation with various forms. We introduce different forms of disinformation, discuss factors related to the spread of disinformation, elaborate on the inherent challenges in detecting disinformation, and show some approaches to mitigating disinformation via education, research, and collaboration. Looking ahead, we present some promising future research directions on disinformation. This article is categorized under:Algorithmic Development > Multimedia Commercial, Legal, and Ethical Issues > Social Considerations Application Areas > Education and Learning

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  • Social bots increase public exposure to created content and boost its perceived credibility by targeting influential users more likely to believe disinformation in the hope of getting them to repost the fabricated content.

    “Next, they try to increase public exposure to the created content and thus boost its perceived credibility by targeting influential users that are more likely to believe disinformation in the hope of getting them to “repost” the fabricated content.”

    From Fake news, disinformation and misinformation in social media: a review (2023) · cites this paper as Shu et al. 2020a

    Section 3.3, “Bots on Social Media,” says bots target people more likely to believe disinformation. It also says bots target influential users in hopes of a repost that increases exposure and perceived credibility. It does not establish that the influential users are the people more likely to believe disinformation. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • Social bots amplify interactions with content as soon as it is created to make it look legitimate and facilitate its spread across social networks.

    “First, they amplify interactions with content as soon as it is created to make it look legitimate and to facilitate its spread across social networks.”

    From Fake news, disinformation and misinformation in social media: a review (2023) · cites this paper as Shu et al. 2020a

    Section 3.3, “Bots on Social Media,” says bots amplify interactions as soon as content is created to help it go viral. It attributes increased perceived credibility to a different strategy: getting influential users to repost the content. The source therefore supports the timing and spread, but not the claim that the initial amplification itself makes the content look legitimate. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Shu, K., Bhattacharjee, A., Alatawi, F., Nazer, T. H., Ding, K., Karami, M., & Liu, H.
Affiliations
  • Illinois Institute of Technology (US)
  • Arizona State University (US)
  • Spotify Boston Massachusetts USA
Corresponding author
Shu, Kai
ORCID
Venue
WIREs Data Mining and Knowledge Discovery
Publisher
Wiley
Published
2020-08-15
Volume
10
Issue
6
ISSN
  • 1942-4787 (ISSN-L)
  • 1942-4795
DOI
10.1002/widm.1385
OpenAlex
W3049488969
Cited by
198
References
15
Citations by year
2026: 20 · 2025: 50 · 2024: 42 · 2023: 43 · 2022: 30 · 2021: 13
FWCI
28.76
Type
journal article
Language
en
Open access
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
  • Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
National Science Foundation (1614576, 1909555)
Topics
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
Keywords
disinformation, social media, misinformation detection, disinformation mitigation, fake news, information literacy, media literacy education

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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