A Deep Learning-based Fast Fake News Detection Model for Cyber-Physical Social Services

With the prevalence of social network service in cyber-Physical space, flow of various fake news has been a rough issue for operators of social service. Although many theoretical outcomes have been produced in recent years, they are generally challenged by processing speed of semantic modeling. To solve this issue, this paper presents a deep learning-based fast fake news detection model for cyber-physical social services. Taking Chinese text as the objective, each character in Chinese text is directly adopted as the basic processing unit. Considering the fact that the news are generally short texts and can be remarkably featured by some keywords, convolution-based neural computing framework is adopted to extract feature representation for news texts. Such design is able to ensure both processing speed and detection ability in scenes of Chinese short texts. At last, some experiments are conducted for evaluation on a real-world dataset collected from a Chinese social media. The results show that the proposal possesses lower training time cost as well as higher classification accuracy compared with baseline methods.(c) 2023 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a deep learning-based fast fake news detection model for cyber-physical social services. It takes Chinese text as the objective and adopts characters as the basic processing unit. By using a convolution-based neural computing framework, it extracts feature representation for news texts, ensuring both processing speed and detection ability for Chinese short texts. Experimental results show that this model has lower training time cost and higher classification accuracy than baseline methods.