社交媒体假新闻检测:基本理论、方法及研究方向

Detection of Fake News on Social Media:Basic Theories,Methods and Research Prospects

社交媒体平台的迅速发展不仅极大增强了信息的可访问性,而且加速了假新闻的传播。假新闻的爆炸性增长不仅损害社会稳定,还会侵蚀公众对媒体的信任。在自然语言处理领域中,假新闻检测是一个关键而富有挑战性的任务。为此,首先给出假新闻的定义,深入分析其特征;其次从新闻内容、社交语境、传播网络和混合方法4个角度对现有假新闻检测方法进行分析与评估,介绍相关模型性能、常用数据集以及评价指标;最后,总结并分析目前假新闻检测研究中存在的问题,提出后续可能的研究方向。

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 content,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.