复杂网络视角下在线健康平台科普文章知识社团发现研究

Knowledge Discovery of Popular Science Articles on Online Health Platform from the Perspective of Complex Network

【目的/意义】随着新冠肺炎疫情的暴发和持续蔓延,人们通过在线健康平台推送的科普文章获取健康知识的需求急剧增加,如何从海量文章中高效发现健康知识是亟待解决的问题。【方法/过程】本文在复杂网络视角下,构建健康科普文章文本关联网络,提出知识发现方法并获得知识社团,实现了在线健康平台科普文章的健康知识发现。然后,借助主题分析、复杂网络分析及情感倾向分析等方法,分析了健康科普文章的语义特征、多尺度结构特征和情感特征,并讨论了健康科普文章内容结构、有用性和健康知识的表达模式等。【结果/结论】研究结果表明,本文提出的知识社团发现方法能够有效发现健康科普文章知识内容,对主题词项的有序组织能够有效表达健康知识,不同尺度上文章的文本结构特征、知识表达等存在差异,且健康科普文章的情感倾向集中在中性偏积极的范围。【创新/局限】在复杂网络的视角下实现健康科普文章的知识发现与知识内容分析,后续可根据病症类型等对健康科普文章进行细分,进一步揭示健康科普文章知识特征。

【Purpose/significance】With the outbreak and continuous spread of the new crown pneumonia epidemic,people's demand for obtaining health knowledge through popular science articles pushed by online health platforms has increased sharply,and how to efficiently discover health knowledge from a large number of articles is an urgent problem to be solved.【Method/process】From the perspective of complex network,this paper constructs a text association network of health popular science articles,proposes knowledge discovery methods and obtains knowledge communities,and realizes the health knowledge discovery of popular science articles on online health platforms.Then,with the help of theme analysis,complex network analysis and emotional tendency analysis,the semantic features,multi-scale structural features and emotional characteristics of health popular science articles are analyzed,and the content structure,usefulness and expression mode of health popular science articles are discussed.【Result/conclusion】The results show that the knowledge community discovery method proposed in this paper can effectively discover the knowledge content of health popular science articles,the orderly organization of subject terms can effectively express health knowledge,and there are differences in the text structure characteristics and knowledge expression of articles at different scales,and the emotional tendency of health popular science articles is concentrated in the neutral and positive range.【Innovation/limitation】Knowledge discovery and knowledge content analysis of popular science articles are realized under the perspective of complex networks,and the subsequent health science articles can be subdivided according to disease types,to further reveal the knowledge characteristics of popular science articles.