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Cited by 221Misinformation and Its ImpactsSpam and Phishing DetectionComplex Network Analysis Techniques

Credibility in social media: opinions, news, and health information—a survey

Viviani, Marco, Pasi, Gabriella·2017·WIREs Data Mining and Knowledge Discovery
DOI 10.1002/widm.1209
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Abstract

In the Social Web scenario, where large amounts of User Generated Content diffuse through Social Media, the risk of running into misinformation is not negligible. For this reason, assessing and mining the credibility of both sources of information and information itself constitute nowadays a fundamental issue. Credibility, also referred as believability, is a quality perceived by individuals, who are not always able to discern with their cognitive capacities genuine information from the fake one. For this reason, in the recent years several approaches have been proposed to automatically assess credibility in Social Media. Most of them are based on data‐driven models, i.e., they employ machine‐learning techniques to identify misinformation, but recently also model‐driven approaches are emerging, as well as graph‐based approaches focusing on credibility propagation. Since multiple social applications have been developed for different aims and in different contexts, several solutions have been considered to address the issue of credibility assessment in Social Media. Three of the main tasks facing this issue and considered in this article concern: (1) the detection of opinion spam in review sites, (2) the detection of fake news and spam in microblogging, and (3) the credibility assessment of online health information. Despite the high number of interesting solutions proposed in the literature to tackle the above three tasks, some issues remain unsolved; they mainly concern both the absence of predefined benchmarks and gold standard datasets, and the difficulty of collecting and mining large amount of data, which has not yet received the attention it deserves. WIREs Data Mining Knowl Discov 2017, 7:e1209. doi: 10.1002/widm.1209 This article is categorized under: Algorithmic Development > Web Mining Application Areas > Science and Technology Technologies > Machine Learning

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14 of 14 references verified0 citation errors · 0 not checkable

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  • For consumers, a generalized but crucial protection mechanism involves developing the capabilities necessary for analyzing and interpreting the legitimacy of online content.

    “For consumers in their everyday lives, a rather generalized but crucial protection mechanism involves developing the capabilities necessary for analyzing and interpreting the legitimacy of online content (Bulger & Davison, 2018; De Paor & Heravi, 2020; Viviani & Pasi, 2017).”

    From Deepfakes: Deceptions, mitigations, and opportunities (2023) · cites this paper as Viviani & Pasi, 2017

  • A consideration of the reputation of the information source, the involvement of trustworthy intermediaries such as experts and/or opinion leaders, and personal confidence based on first-hand experiences will further enhance consumers' protection.

    “A consideration of the reputation of the information source, the involvement of trustworthy intermediaries such as experts and/or opinion leaders, and personal confidence based on first-hand experiences will further enhance their protection (Hwang et al., 2021; Viviani & Pasi, 2017; Westerlund, 2019; Whittaker et al., 2020).”

    From Deepfakes: Deceptions, mitigations, and opportunities (2023) · cites this paper as Viviani & Pasi, 2017

  • Making an evaluation in the digital domain is frequently more complex than in the 'offline' world, where individuals have historically minimized credibility uncertainty based on either the reputation of the knowledge source or personal first-hand experiences.

    “In contrast to the 'offline' world, where individuals have historically minimized credibility uncertainty based on either the reputation of the knowledge source (e.g., experts and/or opinion leaders) or personal first-hand experiences, making an evaluation in the digital domain is frequently more complex (Viviani & Pasi, 2017).”

    From Deepfakes: Deceptions, mitigations, and opportunities (2023) · cites this paper as Viviani & Pasi, 2017

  • The multiplicity of sources involved in the distribution of deceptive content, the absence of information quality requirements and evaluation, the ease of manipulating and altering information, the lack of contextual clarification, and the existence of several potential credibility evaluation objectives make deepfakes very real and potent threats.

    “The multiplicity of sources involved in the distribution of deceptive content, the absence of information quality requirements and evaluation, the ease of manipulating and altering information, the lack of contextual clarification, and the existence of several potential credibility evaluation objectives (i.e., content, source, and medium) make deepfakes very real and potent threats (Viv”

    From Deepfakes: Deceptions, mitigations, and opportunities (2023) · cites this paper as Viviani & Pasi, 2017

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Authors
Viviani, M., & Pasi, G.
Affiliations
University of Milano-Bicocca (IT)
Corresponding author
Viviani, Marco
ORCID
Venue
WIREs Data Mining and Knowledge Discovery
Publisher
Wiley
Published
2017-06-04
Volume
7
Issue
5
ISSN
  • 1942-4787 (ISSN-L)
  • 1942-4795
DOI
10.1002/widm.1209
OpenAlex
W2620789444
Cited by
221
References
14
Citations by year
2026: 6 · 2025: 13 · 2024: 23 · 2023: 37 · 2022: 40 · 2021: 39 · 2020: 24 · 2019: 21 · 2018: 14 · 2017: 3 · 2013: 1
FWCI
44.13
Type
journal article
Language
en
Open access
closed
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
  • Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (publishedVersion)
  • BOA (University of Milano-Bicocca) (submittedVersion)
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
credibility assessment, social media, misinformation, opinion spam detection, review sites, fake news detection, microblogging, spam detection, online health information, graph-based methods, machine learning, gold standard dataset, benchmark

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