Taking fake online consumer reviews seriously
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Deepfakes have a legitimate potential to create commercial opportunities, distinguishing them from other forms of deception such as fake reviews or opinion spam that only produce adverse effects.
“Moreover, deepfakes have a legitimate potential to create commercial opportunities, distinguishing them further from other forms of deception such as fake reviews or opinion spam that only produce adverse effects (Johnson & Diakopoulos, 2021; Kietzmann et al., 2020; Malbon, 2013).”
From Deepfakes: Deceptions, mitigations, and opportunities (2023) · cites this paper as Malbon, 2013
If a business develops deepfakes as a means of providing false information to (or concealing information from) consumers, this may increase levels of consumer distrust.
“Additionally, if a business develops deepfakes as a means of providing false information to (or concealing information from) consumers, this may increase levels of consumer distrust (Malbon, 2013; Wu et al., 2020).”
From Deepfakes: Deceptions, mitigations, and opportunities (2023) · cites this paper as Malbon, 2013
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References 45 · Cited by 183
- Authors
- Malbon, J.
- Affiliations
- Monash University (AU)
- Corresponding author
- Malbon, Justin
- ORCID
- Malbon, Justin
- Venue
- Journal of Consumer Policy
- Published
- 2013-01-23
- Volume
- 36
- Issue
- 2
- Pages
- 139-157
- ISSN
- 0168-7034 (ISSN-L)
- 1573-0700
- OpenAlex
- W2039530600
- Cited by
- 183
- References
- 45
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 14 · 2023: 14 · 2022: 18 · 2021: 23 · 2020: 24 · 2019: 14 · 2018: 14 · 2017: 17 · 2016: 11 · 2015: 8 · 2014: 7 · 2013: 2
- FWCI
- 27.67
- Type
- journal article
- Language
- en
- Open access
- green
- papers.ssrn.com
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Journal of Consumer Policy (publishedVersion)
- SSRN Electronic Journal (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Strategy and Management › Diverse Legal and Medical Studies
- Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
- Social Sciences › Business, Management and Accounting › Strategy and Management › Regulation and Compliance Studies
- Keywords
- fake reviews, consumer reviews, information asymmetry, online marketplace regulation, misleading and deceptive conduct, review authenticity, seller reputation, regulatory oversight, online consumer decision-making
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.
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