Article: Precise identification and traceability of fake e-commerce reviews integrating multimodal semantic understanding Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.35 pp.81 - 102 Abstract: Aiming at the problem that the precise identification and traceability methods for false e-commerce reviews are difficult to comprehensively capture the complex semantics and potential features in the reviews, this paper first uses a text convolutional neural network and a pre-trained model to extract features from multi-dimensional text semantics respectively. By further integrating the features of the reviewers, the model's understanding of the overall semantics is further enhanced. The images posted by users in the comments are subjected to feature extraction using the residual network to obtain the corresponding visual semantic features. Subsequently, multimodal semantic feature fusion will be carried out to identify false comments. The experimental results on large-scale datasets show that the recognition and traceability accuracy rates of the proposed method have increased by at least 0.7% and 1.1% respectively, significantly outperforming the existing methods in the recognition and traceability of false comments. Inderscience Publishers - linking academia, business and industry through research

Title: Precise identification and traceability of fake e-commerce reviews integrating multimodal semantic understanding

Authors: Bing Duan

Addresses: Department of Finance and Business, Qinghai Higher Vocational and Technical Institute, Qinghai, 810799, China

Abstract: Aiming at the problem that the precise identification and traceability methods for false e-commerce reviews are difficult to comprehensively capture the complex semantics and potential features in the reviews, this paper first uses a text convolutional neural network and a pre-trained model to extract features from multi-dimensional text semantics respectively. By further integrating the features of the reviewers, the model's understanding of the overall semantics is further enhanced. The images posted by users in the comments are subjected to feature extraction using the residual network to obtain the corresponding visual semantic features. Subsequently, multimodal semantic feature fusion will be carried out to identify false comments. The experimental results on large-scale datasets show that the recognition and traceability accuracy rates of the proposed method have increased by at least 0.7% and 1.1% respectively, significantly outperforming the existing methods in the recognition and traceability of false comments.

Keywords: e-commerce fake reviews; identification and traceability; multimodal semantics; feature fusion; transformer model.

DOI: 10.1504/IJICT.2026.153004

International Journal of Information and Communication Technology, 2026 Vol.27 No.35, pp.81 - 102

Received: 09 Dec 2025
Accepted: 06 Jan 2026
Published online: 17 Apr 2026 *

Title: Precise identification and traceability of fake e-commerce reviews integrating multimodal semantic understanding

Keep up-to-date