Welcome to Francis Academic Press

Academic Journal of Business & Management, 2024, 6(4); doi: 10.25236/AJBM.2024.060440.

Research on the identification method of fake reviews in e-commerce


Daming Fan

Corresponding Author:
Daming Fan

School of Communication, Qufu Normal University, Rizhao, 276826, China


With the development of e-commerce, many merchants, to lure consumers to buy their own goods, will create the illusion of hot stores and excellent goods by means of false reviews. To identify false reviews, this research paper proposes a false review identification method, which mainly uses entropy weight TOPSIS model, K-Means cluster analysis and logistic regression. The article selects evaluation indexes such as sentiment polarity, text length, review usefulness and rating deviation degree, and calculates the review score by entropy weighting method of TOPSIS model. Subsequently, K-Means clustering was used to categorize the reviews into two groups: customer reviews and machine reviews and validated by logistic regression. The experimental results show that the method has a good ability to recognize false reviews with high accuracy, recall, precision, and AUC value. Taken together, the method provides an effective solution for false comment recognition with practical application potential.


Fake reviews, TOPSIS model, Cluster analysis, Logistic regression

Cite This Paper

Daming Fan. Research on the identification method of fake reviews in e-commerce. Academic Journal of Business & Management (2024) Vol. 6, Issue 4: 276-281. https://doi.org/10.25236/AJBM.2024.060440.


[1] China Internet Network Information Center. The 52nd statistical report on China's Internet development [EB/OL]. [2023-08-28]. https://www.cnnic.cn/n4/2023/0828/c88-10829.html.

[2] Cao Dongwei, Li Shaomei, Chen Hongchang. A GCN-based method for false comment detection [J]. Computer Engineering and Application, 2022, 58(03):181-186.

[3] Shi Yunmei, Yuan Bo, Zhang Le et al. IMTS: Fusion of image and text semantics for fake comment detection [J]. Data Analysis and Knowledge Discovery, 2022, 6(08):84-96.

[4] Hsing Juanjuan. A false comment recognition method based on Markov logic network [J]. Journal of Chinese Information, 2016, 30(05):94-100.

[5] Ye Zicheng, Wang Banghai. Spectral clustering based false comment group detection [J]. Computer Applications and Software, 2021, 38(08):175-181.

[6] Q. Zhang, S. Ji, Q. Fu et al. Buffer group detection and characterization based on weighted comment graph [J]. Computer Applications, 2019, 39(06):1595-1600.