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Wanqiu CUI and Dawei WANG, “Survey on Fake Information Generation, Dissemination and Detection,” Chinese Journal of Electronics, vol. 33, no. 3, pp. 1–11, 2024 doi: 10.23919/cje.2022.00.362
Citation: Wanqiu CUI and Dawei WANG, “Survey on Fake Information Generation, Dissemination and Detection,” Chinese Journal of Electronics, vol. 33, no. 3, pp. 1–11, 2024 doi: 10.23919/cje.2022.00.362

Survey on Fake Information Generation, Dissemination and Detection

doi: 10.23919/cje.2022.00.362
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  • Author Bio:

    Wanqiu CUI was born in Liaoning Province, China, in 1990. She received the Ph.D. degree in computer science and technology from the Beijing University of Posts and Telecommunications, in 2021. She is currently a Lecturer at the People’s Public Security University of China. Her current research interests include social network analysis and network security content detection. (Email: wanqiucui@ppsuc.edu.cn)

    Dawei WANG was born in Shandong Province, China, in 1989. He received the Ph.D. degree in computer software and theory from the Renmin University of China, in 2020. He is an Associate Research Fellow in Institute of Scientific and Technical Information of China. His main research interests include databases, artificial intelligence, and regional innovation strategy. (Email: wangdw1@istic.ac.cn)

  • Corresponding author: Email: wangdw1@istic.ac.cn
  • Received Date: 2022-10-28
  • Accepted Date: 2023-07-03
  • Available Online: 2024-03-22
  • The current booming development of the Internet has put the public in an era of information overload, in which false information is mixed and spread unscrupulously. This phenomenon has seriously disturbed the social network order. Thus, a substantial of research is beginning to be devoted to the effective management of fake information. We analyze the abnormal characteristics of fake information from its mechanism of generation and dissemination. In view of different exceptional features, we systematically sort out and evaluate the existing studies on false content detection. The commonly used public datasets, metrics, and performance are categorized and compared, hoping to provide a basis and guidance for related research. The study found that the current active social platforms show different novelty. The future direction should point to mining platform features of multi-domain sources, multi-data forms, and multi-language heterogeneity to provide more valuable clues for fake information.
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