Affiliations: State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China
Correspondence:
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Corresponding author. Lei Shi. State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China. E-mail: leikyshi@qq.com.
Abstract: Social networks have accelerated the speed and scope of information dissemination. However, the lack of regulation and freedom of speech on social platforms has resulted in the widespread dissemination of the unverified message. Therefore, rapid and effective detection of social network rumors is essential to purify the network environment and maintain public security. Currently, the defects of rumor detection technology are that the detection time is too long and the timeliness is poor. In addition, the differences based on specific regions or specific fields will lead to deviations in the training dataset. In this paper, firstly, the definition of rumor is described, and the current problems and detection process of rumor detection are described; Secondly, introduce different data acquisition methods and analyze their advantages and disadvantages; Thirdly, according to the development of rumor detection technology, the existing rumor detection methods of artificial, machine learning and deep learning are analyzed and compared; Finally, the challenges of social network rumor detection technology are summarized.
Keywords: Rumor, rumor detection, machine learning, deep learning, social networks
