Fake review detection on online E-commerce platforms: a systematic literature review

• Published in: Data mining and knowledge discovery Vol. 35; no. 5; pp. 1830 - 1881
• Main Authors: Paul, Himangshu, Nikolaev, Alexander
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2021; Springer Nature B.V
• Subjects: Academic Surveys and Tutorials; Artificial Intelligence; Chemistry and Earth Sciences; Competition; Computer Science; Consumers; Damage prevention; Data Mining and Knowledge Discovery; Electronic commerce; False information; Information Storage and Retrieval; Information systems; Literature reviews; Physics; Social networks; Spamming; Statistics for Engineering; Systematic review; User behavior; User generated content; Cyber-security; False information; Fraud; Disinformation; Spam detection; Opinion spamming
• ISSN: 1384-5810; 1573-756X
• DOI: 10.1007/s10618-021-00772-6

The increasing popularity of online review systems motivates malevolent intent in competing sellers and service providers to manipulate consumers by fabricating product/service reviews. Immoral actors use Sybil accounts, bot farms, and purchase authentic accounts to promote products and vilify competitors. Facing the continuous advancement of review spamming techniques, the research community should step back, assess the approaches explored to date to combat fake reviews, and regroup to define new ones. This paper reviews the literature on Fake Review Detection (FRD) on online platforms. It covers both basic research and commercial solutions, and discusses the reasons behind the limited level of success that the current approaches and regulations have had in preventing damage due to deceptive reviews.