Detecting Rumors Based on CycleGAN and Wasserstein Loss
[Objective]By improving the generative loss with CycleGAN and Wasserstein distance,this paper enhances the stability and accuracy of the rumor detection model in cases of unbalanced and unpaired data samples.[Methods]We developed the enhanced rumor discriminative model through adversarial training between the generator and discriminator.During the generative training process,we introduced cyclic consistency loss and recognition loss to achieve controllability of the generated target.We improved the model generative loss using Wasserstein distance,avoiding the problem of gradient explosion that may occur during adversarial network training.[Results]Our method's accuracy reached 0.8698,and the F1 score was 0.8550 on the unbalanced rumor dataset PHEME.Compared with the baseline method,it has increased by 0.0068 and 0.0180,respectively.[Limitations]The new rumor detection model only has two generators and can only achieve the conversion of two categories of samples.It is suitable for binary classification rumor detection models and cannot be applied to multi-classification rumor detection tasks.[Conclusions]The proposed model can effectively enhance the ability to detect rumors from imbalanced data.
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