The rapid development of social media platforms not only greatly enhances the accessibility of information,but also accelerates the spread of fake news.The explosive growth of fake news not only damages social stability,but also erodes public trust in the media.In the field of natural language processing,fake news detection is a crucial and challenging task.To this end,first provide a definition of fake news and analyze its characteristics in depth;Then,analyze and evaluate the existing fake news detection methods from four perspectives:news con-tent,social context,communication networks,and hybrid methods.Introduce the performance of relevant models,commonly used datasets,and evaluation indicators;Finally,summarize and analyze the current problems in fake news detection research,and propose possible future research directions.
关键词
社交网络/假新闻/自然语言处理/早期检测/可解释性
Key words
social network/fake news/natural language processing/early detection/explainability
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