摘要
以解决微博平台海量信息碎片为切入点,结合微博信息文本短小、来源广泛、传播方式多样等特点,设计基于中心化的微博热点的发现机制。通过微博平台开放API记录的结构化元数据信息,设计微博的元数据模型,将微博热点发现看作是原始语料到热点语料簇的生产加工增值过程,设计以数据预处理技术为核心的语料初加工方法,以及基于短文本聚类、基于传播路径与用户行为的中心化深加工方法,构建完整的生产加工过程模型,并通过实例验证理论研究成果。
In order to solve the problem of massive pieces of information on micro-blogs, this paper studies the centralization theory-based hotspot discovery methods for micro-blogs, in consideration of the features of micro-blogging content such as short text, variety of sources and diverse means of dissemination. Through the structured metadata acquired from open APIs, some metadata models for micro-blogging content are analyzed, and the hotspot discovery process is regarded as a value-added process of the original materials to clusters of hot products. For initial and deep processing methods during the production process, some data pre-processing techniques as well as short text clusteringbased and disseminating path and users behavior-based centralizing techniques are proposed. And a complete production and processing model is established. Finally, a series of experiments have verified the theoretical achievement.
出处
《管理学报》
CSSCI
北大核心
2012年第6期874-879,共6页
Chinese Journal of Management
基金
国家自然科学基金资助项目(71071066)
教育部人文社会科学研究资助项目(11YJA630098)
关键词
热点发现
微博
中心化
元数据模型
hotspot discovery
micro-blogging
centralization~ metadata model