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Cited by 359

The DOI matches its Crossref record; 174 of the 174 references it cites were found in a registry or an index.

Fake online reviews: Literature review, synthesis, and directions for future research

Open access
Wu, Yuanyuan, Ngai, Eric W.T., Wu, Pengkun, Wu, Chong·2020·Decision Support Systems
DOI 10.1016/j.dss.2020.113280
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Abstract

Abstract Fake online reviews in e-commerce significantly affect online consumers, merchants, and, as a result, market efficiency. Despite scholarly efforts to examine fake reviews, there still lacks a survey that can systematically analyze and summarize its antecedents and consequences. This study proposes an antecedent–consequence–intervention conceptual framework to develop an initial research agenda for investigating fake reviews. Based on a review of the extant literature on this issue, we identify 20 future research questions and suggest 18 propositions. Notably, research on fake reviews is often limited by lack of high-quality datasets. To alleviate this problem, we comprehensively compile and summarize the existing fake reviews-related public datasets. We conclude by presenting the theoretical and practical implications of the current research.

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Citations

Cites

Show all 174 references

Research works printed but not linked: 30

  1. *Luca, 2016, Fake it till you make it: reputation, Competition, and Yelp Review Fraud, Management Science, 62, 3412
  2. *Aslam, 2019, A survey on opinion spam detection methods, Int. J. Sci. Technol. Res., 8, 1355
  3. *Sudhakaran, 2016, A framework investigating the online user reviews to measure the biasness for sentiment analysis, Asian Journal of Information Technology, 15, 1890
  4. *Hu, 2018, Double learning or double blinding: an investigation of vendor private information acquisition and consumer learning via online reviews, Ann. Oper. Res., 270, 213, 10.1007/s10479-016-2243-z · doi:10.1007/s10479-016-2243-z
  5. *Fogel, 2017, Intentions to use an online restaurant review web site and purchase behavior after reading reviews, First Monday, 22, 1
  6. *Fogel, 2018, Intentions to use the TripAdvisor review website and purchase behavior after reading reviews, Hum. IT, 14, 59
  7. *Poongodi, 2019, Recommendation model based on truth relations & user credibility, Journal of Intelligent & Fuzzy Systems, 36, 4057
  8. *Sivaramakrishnan, 2016, Recommendation system with demographic attributes for fake review identification, Research Journal of Pharmaceutical Biological and Chemical Sciences, 7, 891
  9. *Feng, 2018, Effects of review spam in a firm-initiated virtual brand community: evidence from smartphone customers, Inf. Manag., 55, 1061, 10.1016/j.im.2018.05.012 · DOI · Paper page
  10. *Amar, 2019, A network-based spam detection framework for reviews in online social media, International Journal of Innovative Technology and Exploring Engineering, 8, 748
  11. *Saini, 2017, Ensemble learning to find deceptive reviews using personality traits and reviews specific features, J. Digit. Inf. Manag., 15, 84
  12. *Liao, 2017, Detect online review spammers based on comprehensive trustiness propagation model, Journal of Internet Technology, 18, 1
  13. *Xu, 2019, Slanderous user detection with modified recurrent neural networks in recommender system, Inf. Sci., 505, 265, 10.1016/j.ins.2019.07.081 · doi:10.1016/j.ins.2019.07.081
  14. *Khurshid, 2019, Enactment of ensemble learning for review spam detection on selected features, International Journal of Computational Intelligence Systems, 12, 387, 10.2991/ijcis.2019.125905655 · doi:10.2991/ijcis.2019.125905655
  15. *Jalther, 2019, Reputation reporting system using text based classification, International Journal of Innovative Technology and Exploring Engineering, 8, 1555
  16. *Bhutada, 2019, Deep learning framework to detect the false analysis of a product given by robots and malicious users, International Journal of Innovative Technology and Exploring Engineering, 8, 689
  17. *Bhuvaneshwari, 2019, An improved personalized recommender system with optimized ABC neural network, Journal of Advanced Research in Dynamical and Control Systems, 11, 1562
  18. *Rajamohana, 2017, Performance analysis of iBPSO and BFPA based feature selection techniques for improving classification accuracy in review spam detection, Applied Mathematics & Information Sciences, 11, 1149, 10.18576/amis/110422 · doi:10.18576/amis/110422
  19. *Nandhini, 2018, Spam review detection on e-commerce sites, International Journal of Civil Engineering and Technology, 9, 1167
  20. *Deng, 2016, Deceptive reviews detection of industrial product, International Journal of Services Operations and Informatics, 8, 122, 10.1504/IJSOI.2016.080090 · doi:10.1504/ijsoi.2016.080090
  21. *Kamalesh, 2015, Extracting product features from consumer reviews and its applications, Int. J. Appl. Eng. Res., 10, 2345
  22. *Baraithiya, 2019, Classifiers ensemble for fake review detection, International Journal of Innovative Technology and Exploring Engineering, 8, 730
  23. *Asha, 2016, Ranking the product details and its application using sentiment classification, Research Journal of Pharmaceutical Biological and Chemical Sciences, 7, 1399
  24. *Gu, 2017, Detecting spammers using review graph, International Journal of High Performance Computing and Networking, 10, 269
  25. *Kolhar, 2018, E-commerce review system to detect false reviews, Sci. Eng. Ethics, 24, 1577, 10.1007/s11948-017-9959-2 · doi:10.1007/s11948-017-9959-2
  26. *Jawaid, 2019, Predilection decoded: web based spam detection and review analysis for online portals, International Journal of Recent Technology and Engineering, 8, 2773
  27. *Rani, 2018, Online social networking services and spam detection approaches in opinion mining-a review, International Journal of Web Based Communities, 14, 353, 10.1504/IJWBC.2018.096245 · doi:10.1504/ijwbc.2018.096245
  28. Wasserman, 2017, Fake news from Africa: panics, politics and paradigms, Journalism, 1
  29. *Wang, 2016, Detecting deceptive review spammers based on pseudo-supervised classification, ICIC Express Letters, 10, 2269
  30. *Chaudhary, 2019, Quantify the reviewer genunity based on behavior metrics and past trust analysis, Int. J. Sci. Technol. Res., 8, 1116

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Metadata

Authors
Wu, Y., Ngai, E. W. T., Wu, P., & Wu, C.
Venue
Decision Support Systems
Publisher
Elsevier BV
Published
2020-05
Volume
132
Pages
113280
ISSN
0167-9236 (ISSN-L)
DOI
10.1016/j.dss.2020.113280
OpenAlex
W3009436785
Cited by
359
References
174
FWCI
48.35
Type
journal article
Language
en
Open access
green
Licence
  • www.elsevier.com/tdm/userlicense/1.0/ (tdm)
  • www.elsevier.com/legal/tdmrep-license (tdm)
Funders
  • Ministry of Education of China
  • Natural Science Foundation of Hunan Province (2019JJ50403)
  • Fundamental Research Funds for the Central Universities

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CoChat. (2026). Citation check: Fake online reviews: Literature review, synthesis, and directions for future research. https://app.cochat.ai/papers/10.1016/j.dss.2020.113280
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Wu, Y., Ngai, E. W. T., Wu, P., & Wu, C. (2020). Fake online reviews: Literature review, synthesis, and directions for future research. Decision Support Systems. https://doi.org/10.1016/j.dss.2020.113280

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.