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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (6): 55-70    DOI: 10.11925/infotech.2096-3467.2021.1259
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Identifying Fake Accounts with User-Review-Shop Relationship and User Deviation Analysis
Meng Yuan(),Wang Yue
School of Statistics and Information, Shanghai University of International Business and Economics, Shanghai 201620, China
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Abstract  

[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.
Key words： User Deviation      Reinforcing Relationship      Fake User Identification      Mean Deviation      Fake Degree     
Received: 04 November 2021      Published: 28 July 2022
ZTFLH:  TP391  
Fund:Shanghai Philosophy and Social Sciences Planning Project(2020BGL009);Graduate Research Innovation Cultivation Project of Shanghai University of International Business and Economics(2021-030800-05)
Corresponding Authors: Meng Yuan     E-mail: nancymeng@suibe.edu.cn
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Cite this article:

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.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.1259     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I6/55

Design of URS-FDIRM
类型 指标缩写 指标个数
用户虚假度指标 用户活跃等级（UL）
用户社交粉丝数（UF）
用户提问与回答比（UQA）
时间间隔（UTS）
用户突发评论数（URB）
用户评论次数（URN）
用户评论频率（URF）
用户评论集中度（URC）
用户在商户中的评论集中度（USC）		 9
评论虚假度指标 评论长度（RL）
极端评分（RR）
评论图片数（RPN）
评论相似度（RS）		 4
商户虚假度指标 商户年龄（SA）
商户规模（SS）
商户早期评论数（SRN）
商户高评论用户数（SUN）		 4
Indicators Related to User-Review-Shop Fake Degree
Reinforced Relationship of URS
Process of Fake Users Identification
Cumulative Distribution of Number of User Reviews and Number of Users
对象 指标 描述 最小值 最大值 中位数 均值 标准差
用户 UL 用户活跃等级 6.000 0 45.000 0 17.000 0 16.840 0 3.460 0
UF 用户社交粉丝数 0.000 0 5 927.000 0 1 182.000 0 960.820 0 520.730 0
UQA 用户提问与回答比 0.000 0 1.000 0 0.000 0 0.040 7 0.156 6
UTS 用户评论时间间隔 1.000 0 4 087.000 0 2 049.000 0 1 985.270 0 433.970 0
URB 用户突发评论数 1.000 0 132.000 0 2.000 0 2.780 0 7.290 0
URN 用户评论次数 51.000 0 301.000 0 54.000 0 56.600 0 12.600 0
URF 用户评论频率 0.014 2 64.000 0 0.026 9 0.093 0 1.539 6
URC 用户评论集中度 0.012 2 0.987 6 0.037 0 0.043 0 0.066 2
USC 用户在商户中评论集中度 0.181 8 1.000 0 1.000 0 0.994 3 0.022 5
评论 RL 评论长度 0.000 0 576.000 0 9.000 0 12.470 0 13.330 0
RR 极端评分 0.000 0 5.000 0 5.000 0 4.430 0 0.770 0
RPN 图片数 0.000 0 10.000 0 0.000 0 0.002 0 0.058 0
RS 评论相似度 0.000 0 1.000 0 0.028 0 0.039 0 0.045 0
商户 SA 商户年龄 0.000 0 108.000 0 8.000 0 9.807 6 7.612 7
SS 商户规模 1.000 0 1 500.000 0 80.000 0 102.070 0 92.610 0
SRN 商户早期评论数 1.000 0 73.000 0 5.000 0 7.596 5 9.910 7
SUN 商户高评论用户数（≥50） 2.000 0 73.000 0 5.000 0 7.551 3 9.782 0
Descriptive Statistics
Distribution of User-Review and Shop-User
检验对象 |cor| |T value| P value
迭代前 迭代后 迭代前 迭代后 迭代前 迭代后
用户-评论虚假度 0.379 7 0.051 3 17.544 0 2.197 1 0.000 0*** 0.028 1***
商户-用户虚假度 0.094 8 0.098 3 11.075 0 11.488 0 0.000 0*** 0.000 0***
评论-商户虚假度 0.458 2 0.458 2 165.300 0 165.300 0 0.000 0*** 0.000 0***
Correlation Coefficient Test
Deviation Display
User Fake Degree Deviation Analysis
规则 规则说明
1 如果一个用户的评论总是与对应商户中其他用户的评论差别很大,这个用户是可疑的。例如,如果一个用户总是给他评论过的商户很高的评价,而其余用户给这些商户的评价较低,此时该用户是可疑的[10]。
2 如果一个用户的评论总是与对应商户中其他用户早已发布的评论很相似,这个用户是可疑的。因为虚假用户往往会复制他人已有的评论达到快速评论提高影响的目的[15]。
3 如果一个用户绝大多数评论都集中在某一家或某几家商户,且总是发布好评或差评,这个用户是可疑的。此时很可能存在用户与商户之间的串谋关系[18]。
4 鉴于本文所用的数据为酒店数据,具有其特殊性,如果一个用户在一天内发布了大量评论,这个用户是可疑的。
5 仅仅从评论文本观察,如果一个用户的评论总是遵循一个固定的模板,或是毫无逻辑的辞藻堆砌,这个用户是可疑的。
6 进入用户主页,观察用户相关数据及日常行为,主观感受该用户是否可疑。
7 在进行数据标注时,要综合所有相关信息进行考量,不可仅看一个方面做出想当然的判断。
Tagging Rules
模型 F1值
URS-FDIRM_MEAN 0.914 0
URS-FDIRM_JS 0.878 1
URS-FDIRM_KL 0.874 6
URS-FDIRM_WITHOUT_DEV 0.871 0
The Experimental Results
k P R F1
230 0.961 0 0.795 7 0.870 6
240 0.954 4 0.824 4 0.884 6
250 0.944 2 0.849 5 0.894 3
260 0.934 9 0.874 6 0.903 7
270 0.922 5 0.896 1 0.909 1
280 0.914 6 0.921 1 0.917 9
290 0.903 8 0.942 7 0.922 8
300 0.890 4 0.960 6 0.924 1
310 0.865 0 0.964 2 0.911 9
320 0.844 2 0.971 3 0.903 3
330 0.818 7 0.971 3 0.888 5
340 0.797 7 0.974 9 0.877 4
Experimental Results Under Different k Values
Change of F1 Score
λ F1 λ F1
0.0 0.871 0 0.6 0.591 4
0.1 0.914 0 0.7 0.483 9
0.2 0.910 4 0.8 0.344 1
0.3 0.842 3 0.9 0.222 2
0.4 0.770 6 1.0 0.179 2
0.5 0.706 1
F1 Score Under Different λ
模型 P R F1
URS-FDIRM_MEAN 0.893 3 0.960 6 0.925 7
LR 0.881 5 0.474 1 0.613 1
RF 0.720 5 0.131 6 0.221 6
KNN 0.939 2 0.737 0 0.825 7
DNN 1.000 0 0.750 0 0.857 1
Fsum 0.262 9 0.709 7 0.383 7
Algorithm Performance
指标 Mean sq F value Pr（>F）
UL 0.205 0 35.490 0 0.000 0***
UF 0.314 1 41.600 0 0.000 0***
UQA 8.133 0 405.000 0 0.000 0***
UTS 0.528 2 55.310 0 0.000 0***
URB 0.134 0 44.960 0 0.000 0***
URN 0.045 1 26.080 0 0.000 0***
URF 0.000 0 0.044 0 0.833 0
URC 0.281 8 64.980 0 0.000 0***
USC 0.015 3 30.550 0 0.000 0***
RL 0.000 0 0.523 0 0.470 0
RR 0.013 8 0.745 0 0.388 0
RPN 0.000 0 1.854 0 0.173 0
RS 0.000 2 2.666 0 0.103 0
SA 0.005 2 32.170 0 0.000 0***
SS 0.006 0 32.020 0 0.000 0***
SRN 0.044 4 20.720 0 0.000 1***
SUN 0.046 0 21.990 0 0.000 0***
ANOVA Summary
删去的指标 新模型的F1值
URF 0.896 1
RL 0.408 6
RR 0.860 2
RPN 0.881 7
RS 0.863 8
Algorithm Performance Without Some Indicators
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