IMTS: Detecting Fake Reviews with Image and Text Semantics
Shi Yunmei1,2,Yuan Bo1,2,Zhang Le1,2(),Lv Xueqiang1
1Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, Beijing 100101, China 2School of Computer Science, Beijing Information Science and Technology University, Beijing 100101, China
[Objective] This paper proposes a fake comment detection method (IMTS) integrating image information and text semantics for Chinese e-commerce websites, aiming to address the proliferation of fake comments posted by “Internet Water Army”. [Methods] First, we used the text convolutional neural network (TextCNN) and the BERT pre-training model to extract features of the text review information, and obtained the corresponding feature vectors. Then, we integrated the reviewer features to enhance the model’s capture of the overall semantic information by splicing the review text semantics and the output features of the reviewer ID. Third, we used the Residual Network (ResNet) to extract features from pictures posted by users in comments to obtain corresponding visual features. Finally, we conducted multimodal fusion of text features and visual features to detect the fake comments. [Results] The IMTS method achieved 96.36% accuracy, 96.35% recall and 96.35% F1 value on the self-built multimodal Chinese fake comment dataset. [Limitations] The dataset in this paper was small in scale, and the BERT pre-training model was used in the text processing stage. [Conclusions] The proposed method could effectively improve the overall detection accuracy of fake comments.
施运梅, 袁博, 张乐, 吕学强. IMTS：融合图像与文本语义的虚假评论检测方法*[J]. 数据分析与知识发现, 2022, 6(8): 84-96.
Shi Yunmei, Yuan Bo, Zhang Le, Lv Xueqiang. IMTS: Detecting Fake Reviews with Image and Text Semantics. Data Analysis and Knowledge Discovery, 2022, 6(8): 84-96.
(China Internet Network Information Center. Statistical Report of the 47th Chinese Internet Development[R/OL]. [2021-02-28]. http://www.cac.gov.cn/2021-02/03/c_1613923423079314.htm.)
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