A Research on Cross-Language Fake Reviews Identification Based on ERNIE and SGAN
2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS)(2023)
摘要
With the rise of social media, the prevalence of fake reviews has surged, causing significant harm to the e-commerce industry's competitive landscape. To tackle the issue of limited publicly available datasets and the challenge of identifying fake reviews, a cross-lingual review dataset is created by amalgamating existing publicly available datasets. A fake review recognition model is devised based on the ERNIE2.0 pretrained language model and a semi-supervised generative adversarial network. Initially, ERNIE is employed to extract high-quality linguistic representations of the review data. Next, a generator in a semi-supervised generative adversarial network is utilized to generate noisy data that has a similar distribution to that of the genuine review text data. Finally, the identification of fake reviews is executed in a discriminator. Experimental validation is conducted using the cross-lingual dataset created, and the results indicate that the method achieves a remarkable 81.43% accuracy in identifying fake reviews with only a small amount of labeled data, thereby affirming its effectiveness.
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关键词
component,Fake reviews,Cross-language reviews,ERNIE2.0,Generating Adversarial Networks
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