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作者:

Li, Jia (Li, Jia.) | Lei, Minglong (Lei, Minglong.)

收录:

EI Scopus

摘要:

Social networks have become indispensable in people's lives. Despite the conveniences brought by social networks, the fake news on those online platforms also induces negative impacts and losses for users. With the development of deep learning technologies, detecting fake news in a data-driven manner has attracted great attention. In this paper, we give a brief survey that discusses the recent development of deep learning methods in fake news detection. Compared with previous surveys, we focus on the different data structures instead of the models they used to process those data. We give a new taxonomy that categorizes current models into the following three parts: models that formulate fake news detection as text classification, models that formulate fake news detection as graph classification, and models that formulate fake news detection as hybrid classification. The advantages and drawbacks of those methods are also discussed. © 2022 The Author(s).

关键词:

Fake detection Social networking (online) Text processing Deep learning Modal analysis Learning systems Classification (of information)

作者机构:

  • [ 1 ] [Li, Jia]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Lei, Minglong]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Lei, Minglong]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing; 100124, China

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年份: 2022

期: C

卷: 214

页码: 1339-1344

语种: 英文

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SCOPUS被引频次: 12

ESI高被引论文在榜: 0 展开所有

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