随着计算机的普及与互联网的高速发展,Facebook、Twitter、新浪微博等社交媒体逐渐成为人们信息交流的主要渠道。然而,由于社交媒体信息具有数量庞大、结构复杂、传播速度快等特点,人们无法从中快速准确地获取想要的信息。于是,话题检测与追踪技术应运而生,它将用户关注的信息从大量无序信息中筛选出来,经过细致的过滤和有效的整合,生成简单、清晰的话题信息,并在此基础上实现对话题的追踪和发展趋势分析。该文对社交媒体上的话题检测与追踪工作进行综述,首先论述了话题检测方面的三类方法,包括基于主题模型的话题检测、基于改进聚类算法的话题检测和基于多特征融合的话题检测;其次,对话题追踪的研究成果进行了介绍,主要分为非自适应话题追踪和自适应话题追踪两大类;最后,列举出社交媒体话题的检测与追踪中存在的问题以及对未来研究的展望。
Abstract
Social media such as Facebook, Twitter, and Sina Microblog have become the main channels for people to exchange information. To deal with the large quantity, complex structure and the fast transmission speed of social media information, the technology of topic detection and tracking comes into being to generate simple and clear topic information. This paper reviews the work done on social media topic detection and tracking. Firstly, it summarizes three types of topic detection methods based on topic model, clustering algorithm and multi-feature fusion, respectively. Secondly, it introduces the researches on topic tracking in two categories: non adaptive topic tracking and adaptive topic tracking. Finally, it lists the problems in the current topic detection and tracking technology, and discusses the prospects of future researches on social media.
关键词
话题检测 /
话题追踪 /
聚类 /
主题模型
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Key words
topic detection /
topic tracking /
clustering /
topic model
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脚注
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基金
国家自然科学基金(61772081,61602044);科技创新服务能力建设—科研基地建设—北京实验室—国家经济安全预警工程北京实验室项目(PXM2018_014224_000010)
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