The DOI matches its Crossref record; 89 of the 89 references it cites were found in a registry or an index.
Fake news, social media and marketing: A systematic review
Open accessAbstract
There is growing concern amongst policy makers, managers and academic researchers over the role that social media plays in spreading misinformation, widely described as ‘Fake News’. However, research to date has mainly focussed on the implications of fake news for political communication and debate. There has been less focus on the implications of social media misinformation upon marketing and consumers. Given the key role of social media as a communication platform, there is a gap in our understanding of fake news through a consumer lens. We address this gap by conducting an interdisciplinary systematic review of the relevant literature. Through critical evaluation and synthesis of the literature, we identify five themes that explain the fake news phenomenon: the dissemination process, spreading channel features, outcomes, fabricated legitimacy and attitudes. Finally, we propose a theoretical framework that highlights themes’ relationships and research propositions to guide future research in this area.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Literature review as a research methodology: An overview and guidelines
Snyder, H. (2019) · Journal of Business Research · doi:10.1016/j.jbusres.2019.07.039 · Cited by 11,524
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
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
Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004) · Journal of Interactive Marketing · doi:10.1002/dir.10073 · Cited by 5,916 · cited 1×
“In this sense, individuals share information to show concern for others (Hennig-Thurau et al., 2004) and to try to help others (Lovett, Peres, & Shachar, 2013).”
BackgroundLazer, 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
Misinformation and Its Correction: Continued Influence and Successful Debiasing
Lewandowsky, S., Ecker, U. K. H., Seifert, C. M., Schwarz, N., & Cook, J. (2012) · Psychological Science in the Public Interest · doi:10.1177/1529100612451018 · Cited by 2,920
The spreading of misinformation online
Del Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., Stanley, H. E., & Quattrociocchi, W. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1517441113 · Cited by 2,407
The art of writing literature review: What do we know and what do we need to know?
Paul, J., & Criado, A. R. (2020) · International Business Review · doi:10.1016/j.ibusrev.2020.101717 · Cited by 2,347
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
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
A Comprehensive Survey on Deep Graph Representation Learning
Ju, W., Fang, Z., Gu, Y., Liu, Z., Long, Q., Qiao, Z., Qin, Y., Shen, J., Sun, F., Xiao, Z., et al. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106207 · Cited by 341
Lampropoulos, G., Keramopoulos, E., Diamantaras, K., & Evangelidis, G. (2022) · Education Sciences · doi:10.3390/educsci12110798 · Cited by 119
Kops, M., Schittenhelm, C., & Wachs, S. (2025) · Computers in Human Behavior · doi:10.1016/j.chb.2025.108650 · Cited by 39
Impact of Celebrity Endorsement and Breaking News Effect on the Attention of Consumers
Lazar, L., & Pop, M.-I. (2021) · Studia Universitatis „Vasile Goldis” Arad – Economics Series · doi:10.2478/sues-2021-0014 · Cited by 10
Metadata
- Authors
- Domenico, G. D., Sit, J., Ishizaka, A., & Nunan, D.
- Venue
- Journal of Business Research
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 124
- Pages
- 329-341
- ISSN
- 0148-2963 (ISSN-L)
- OpenAlex
- W3111430045
- Cited by
- 431
- References
- 89
- FWCI
- 13.80
- Type
- journal article
- Language
- en
- Open access
- green
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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Fake news, social media and marketing: A systematic review. https://app.cochat.ai/papers/10.1016/j.jbusres.2020.11.037
- The paper
- Domenico, G. D., Sit, J., Ishizaka, A., & Nunan, D. (2021). Fake news, social media and marketing: A systematic review. Journal of Business Research. https://doi.org/10.1016/j.jbusres.2020.11.037
Data sources
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.