复杂网络视角下在线健康社区评论有用性研究

A Study on the Usefulness of Comments in Online Health Community Based on Complex Network Perspective

【目的/意义】在线健康社区用户规模庞大,信息量浩如烟海,如何帮助社区管理者和用户判别有用信息,提高决策效率是亟待解决的问题。【方法/过程】在复杂网络视角下,提出一个新的评论有用性分析框架。首先,采集在线健康社区患者评论数据,采用文本分析法分析有用评论、非有用评论以及所有评论的主题分布和情感分布,初步分析各类评论文本的有用性特征;其次,将各类评论文本分别转换为文本关联网络,使用社会网络分析方法进一步分析其有用性特征;最后,分析评论有用性及其特征与患者发表评论、用户对评论的有用性投票以及文本关联网络结构特征的关联性,实现基于文本关联网络的评论有用性分析。【结果/结论】有用评论和非有用评论文本关联网络结构具有一定差异,在线健康社区用户就诊前后的信息需求和经验输出的重点有所不同。【创新/局限】基于复杂网络视角研究在线健康社区评论有用性,但仅使用了好大夫在线的数据,未来可对更多数量和种类的在线健康社区信息内容有用性进行研究。

【Purpose/significance】With the huge scale of online health community users and a vast amount of information,how to help community managers and users to discern useful information and improve the efficiency of decision making is an urgent problem.【Method/process】A new comment usefulness analysis framework is proposed under the perspective of complex networks.First,collect the data of patients’ comments in online health communities,use text analysis to analyze the thematic and emotional distribution of useful comments,non-useful comments and all comments,and analyze the usefulness characteristics of various types of comment texts preliminarily;secondly,transform all kinds of comment texts into text association networks,and use social network analysis method to analyze their usefulness ulteriorly;finally,analyze the correlation between the usefulness of comments,their characteristics and the patient comments,user votes on the usefulness of the comments,structural features of text association network to implement review usefulness analysis based on text association network.【Result/conclusion】There are some differences in the text association network structure between useful and non-useful comments,and the focus of information needs and experience output of users before and after consultation in online health communities is different.【Innovation/limitation】Based on a complex network perspective,study the usefulness of online health community comments,but only use the data from haodf.com.To study the usefulness of information content of a larger number and variety of online health communities in the future.