Fake Comment Detection Based on Generative Adversarial Networks

doi:10.1007/978-981-97-2200-6_8

	Fake Comment Detection Based on Generative Adversarial Networks
	Deng, Xiaofei; Cao, Tianya; Chen, Jian; Li, Jingwei
	2024
会议名称	3rd International Conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2023
会议录名称	Lecture Notes in Electrical Engineering
卷号	1172 LNEE
页码	85-95
会议日期	December 22, 2023 - December 24, 2023
会议地点	Xiamen, China
出版者	Springer Science and Business Media Deutschland GmbH
摘要	Fake comments are a widespread problem in various online activities. This issue not only impacts the consumer experience and service quality but also makes it difficult to distinguish genuine information from the vast pool of comments. Therefore, automated detection of fake comments has become crucial, and this challenging task has garnered considerable attention. Machine learning and deep learning have offered several effective approaches for automatically detecting fake comments, with deep learning, in particular, significantly advancing the field of fake comment detection. However, deep learning often requires a substantial amount of labeled data as support, and the lack of high-quality labeled data has long been a major obstacle in fake comment detection. To address the issue of insufficient labeled data, generative adversarial networks (GANs) have been introduced to compensate for the shortage by generating high-quality fake samples. This method utilizes a limited number of labeled samples and generated fake samples for adversarial learning, thereby enhancing the performance of fake comment detection methods. In this approach, a specific discriminator network structure is designed, combined with long short-term memory networks (LSTM), to optimize feature propagation between network layers. This approach not only maximizes the learning of contextual features but also helps alleviate the potential performance degradation that deep networks may encounter. Ultimately, the proposed method is evaluated using two publicly available high-quality fake comment datasets. The results show that this method achieves a high validation accuracy of 97.87% and an average validation accuracy of 91.04%. Furthermore, metrics such as F1-score, recall, and precision also outperform other comparative methods, with average values of 91.26%, 92.4%, and 95.65%, respectively. A comprehensive analysis of various evaluation metrics suggests that this method outperforms other approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
关键词	Brain Fake detection Generative adversarial networks Network layers Quality control Automated detection Deep learning Fake comment High quality Labeled data Long short-term memory network Machine-learning Memory network Online activities Service Quality
DOI	10.1007/978-981-97-2200-6_8
收录类别	EI
语种	英语
EI入藏号	20242816681698
EI主题词	Long short-term memory
EI分类号	461.1 Biomedical Engineering ; 723 Computer Software, Data Handling and Applications ; 723.4 Artificial Intelligence ; 903.1 Information Sources and Analysis ; 913.3 Quality Assurance and Control
原始文献类型	Conference article (CA)
EISSN	1876-1119
ISSN	1876-1100
文献类型	会议论文
条目标识符	https://ir.nwnu.edu.cn/handle/39RV6HYL/98799
专题	实体学院_计算机科学与工程学院
通讯作者	Deng, Xiaofei
作者单位	College of Computer Science and Engineering, Northwest Normal University, Gansu, Lanzhou; 730070, China
第一作者单位	计算机科学与工程学院
通讯作者单位	计算机科学与工程学院
第一作者的第一单位	计算机科学与工程学院
推荐引用方式 GB/T 7714	Deng, Xiaofei,Cao, Tianya,Chen, Jian,et al. Fake Comment Detection Based on Generative Adversarial Networks[C]:Springer Science and Business Media Deutschland GmbH,2024:85-95.