[Objective] Based on the user-review-shop (URS) and the fake degree relationship, this paper proposes a model based on user deviation, aiming to effectively identify fake accounts. [Methods] First, we measured the user’s deviations of contents and behaviors with the means method, JS divergence and KL divergence respectively. Then, we constructed the URS-FDIRM model to identify fake users with experimental data from mafengwo.com. [Results] The proposed models effectively measured the user’s deviations of contents and behaviors. The F1 value of URS-FDIRM model reached 92.57%. [Limitations] This method mainly uses the conventional measurements to extract the deviation index and did not include more deviation measurements with user behaviors. [Conclusions] The proposed method could help us reveal the false relationship among users, reviews and shops, and monitor abnormal user behaviors.
孟园, 王悦. 基于用户-评论-商户关系的虚假用户识别研究：用户偏差分析的视角[J]. 数据分析与知识发现, 2022, 6(6): 55-70.
Meng Yuan, Wang Yue. Identifying Fake Accounts with User-Review-Shop Relationship and User Deviation Analysis. Data Analysis and Knowledge Discovery, 2022, 6(6): 55-70.
(Deng Song, Wan Changxuan, Guan Aihao, et al. Deceptive Reviews Detection of Technology Products Based on Behavior and Content[J]. Journal of Chinese Computer Systems, 2015, 36(11): 2498-2503.)
Mukherjee A, Kumar A, Liu B, et al. Spotting Opinion Spammers Using Behavioral Footprints[C]// Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2013: 632-640.
Xu Q K, Zhao H. Using Deep Linguistic Features for Finding Deceptive Opinion Spam[C]// Proceedings of the 24th International Conference on Computational Linguistics. ACL, 2012:1341-1350.
Feng S, Banerjee R, Choi Y. Syntactic Stylometry for Deception Detection[C]// Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics. ACL, 2012:171-175.
Goswami K, Park Y, Song C. Impact of Reviewer Social Interaction on Online Consumer Review Fraud Detection[J]. Journal of Big Data, 2017, 4: 15.
Wang X P, Liu K, Zhao J. Handling Cold-Start Problem in Review Spam Detection by Jointly Embedding Texts and Behaviors[C]// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. ACL, 2017: 366-376.
Wang G, Xie S H, Liu B, et al. Identify Online Store Review Spammers via Social Review Graph[J]. ACM Transactions on Intelligent Systems and Technology, 2012, 3(4): 1-21.
(Yu Chuanming, Feng Bolin, Zuo Yuheng, et al. An Individual-Group-Merchant Relation Model for Identifying Online Fake Reviews[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2017, 53(2): 262-272.)
Liu Y C, Pang B. A Unified Framework for Detecting Author Spamicity by Modeling Review Deviation[J]. Expert Systems with Applications, 2018, 112: 148-155.
Shan G H, Zhou L N, Zhang D S. From Conflicts and Confusion to Doubts: Examining Review Inconsistency for Fake Review Detection[J]. Decision Support Systems, 2021, 144: 113513.