基于改进生成对抗网络的谣言检测方法

传统谣言检测算法存在提取文本语义、关键特征等效果不理想的问题,而一般序列模型在文本检测中无法解决特定语义下的特征提取,导致模型泛化能力差。为解决上述问题,该文提出一种改进的生成对抗网络模型(TGBiA)用于谣言检测,该模型采用对抗训练方式,符合谣言在传播过程中人为增删、夸大和歪曲信息的特点,通过对抗网络生成器和判别器的相互促进作用,强化谣言指示性特征的学习,不断提高模型的学习能力。训练过程中的生成器通过Transformer结构代替单一的RNN网络,实现语义的提取和特征的学习,同时,在训练过程中的判别器采用基于双向长短期记忆单元的深度网络分类模型,并引入注意力机制来提升对较长时间序列谣言的判断能力。在公开的微博和Twitter数据集上的实验结果表明,该文提出的方法比其他现有方法检测效果更好,鲁棒性更强。

An Improved Generative Adversarial Network for Rumor Detection

Existing rumor detection algorithms, including general sequential models, are defected in capturing text semantics and key features detection, resulting in poor generalization capability. To address this issue, this paper proposes an improved generative adversarial network model named TGBiA for rumor detection. TGBiA adopts adversarial training method, to capture the development of augmentation, detraction, exaggeration and distortion during its spread. Generator model extracts sequence semantics and features via Transformer instead of RNN. And the discriminator is a classification model based on BiLSTM, with the attention mechanism introduced. Through the mutual promotion of the generator and discriminator, it enables the learning of the indicative features of rumors increasingly. Experimental results on the Weibo and Twitter datasets show that the proposed method is not only outperforms other existing detecting methods but is also more robust.