[Objective]Aiming at the problem of the proliferation of fake comment information published by "Internet Water Army" on e-commerce websites, a fake comment detection method (IMTS) that integrates image information and text semantics for Chinese e-commerce website comments is integrated.

[Methods]The IMTS method uses the text convolutional neural network (TextCNN) and the Bert pre-training model to extract the features of the text review information, and obtain the corresponding feature vectors. The reviewer features are then integrated, and the model's capture of the overall semantic information is further enhanced by splicing the review text semantics and the output features of the reviewer ID. Then use Residual Network (ResNet) to extract features from pictures posted by users in comments to obtain corresponding visual features, and finally perform multimodal fusion of text features and visual features to detect false comments.

[Results]The IMTS method achieves 96.36% accuracy, 96.35% recall and 96.35% F1 value on the self-built multimodal Chinese fake comment dataset.

[Limitations]Due to the limitation of computing power, the dataset in this paper is small in scale, and the Bert pre-training model is used in the text processing stage. In the case of large-scale data computing, the time cost is high.

[Conclusions] It is effective to use multi-modal thinking and feature fusion method to supplement the fake comment text to detect fake comments. This method can effectively improve the overall detection accuracy of fake comments.