图书情报工作

图书情报工作 >

2023 , Vol. 67 >Issue 16: 124 - 134

DOI: https://doi.org/10.13266/j.issn.0252-3116.2023.16.012

知识组织

在线医疗社区健康焦虑用户画像模型构建及实证研究

  • 高靖超 ,
  • 彭丽徽 ,
  • 张艳丰 ,
  • 蒋欣 ,
  • 洪闯
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  • 湘潭大学公共管理学院 湘潭 411105
高靖超，硕士研究生；张艳丰，副教授，博士，硕士生导师；蒋欣，硕士研究生；洪闯，讲师，博士，硕士生导师。

收稿日期: 2023-03-06

  修回日期: 2023-06-13

  网络出版日期: 2023-08-18

基金资助

本文系国家哲学社会科学基金青年项目“数字乡村背景下农村医疗健康信息协同与智慧服务模式研究”(项目编号：22CTQ023)研究成果之一。

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Construction and Empirical Research of Health Anxiety User Portrait Model in Online Medical Community

  • Gao Jingchao ,
  • Peng Lihui ,
  • Zhang Yanfeng ,
  • Jiang Xin ,
  • Hong Chuang
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  • School of Pubic Management, Xiangtan University, Xiangtan 411105

Received date: 2023-03-06

  Revised date: 2023-06-13

  Online published: 2023-08-18

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摘要

[目的/意义] 通过构建在线医疗社区健康焦虑用户画像，以探究健康焦虑用户情感及其他特征表现，为在线医疗平台管理健康焦虑用户及加强在线医疗社区建设提供指导。[方法/过程] 首先，依据现有研究成果并结合在线医疗社区健康焦虑用户实际数据特征建立用户画像标签体系。其次，根据标签体系对情感、主题、信息行为标签进行技术加工，得到标签属性并建立用户画像概念模型。最后，通过K-means聚类方法得到4个具有显著差异性的用户画像群体结构进行实证分析。[结果/结论] 根据用户画像标签特征，将在线医疗社区健康焦虑用户依据情感划分为恐惧型、愤怒型、抑郁型、厌恶型4类，针对每个类型用户画像的显著特征进行具体分析，为在线医疗社区中健康焦虑现象提供一个较为全面的用户画像标签类型解释。

关键词： 在线医疗社区; 健康焦虑; 用户画像; K-means

本文引用格式

高靖超 , 彭丽徽 , 张艳丰 , 蒋欣 , 洪闯 . 在线医疗社区健康焦虑用户画像模型构建及实证研究[J]. 图书情报工作, 2023 , 67(16) : 124 -134 . DOI: 10.13266/j.issn.0252-3116.2023.16.012

Abstract

[Purpose/Significance] By constructing a user portrait of health anxiety in online medical community, this paper explores the emotional and other characteristics of health anxiety users, and provides guidance for online medical platform to understand health anxiety users and strengthen the construction of online medical community. [Method/Process] Firstly, based on the existing research results and the actual data characteristics of health anxiety users in the online medical community, a user portrait label system was established. Secondly, according to the label system, the emotional, thematic, and information behavior labels were technically processed to obtain label attributes and establish a user portrait conceptual model. Finally, four user portrait group structures with significant differences were obtained by K-means clustering method for empirical analysis. [Result/Conclusion] According to the characteristics of user portrait labels, the users with health anxiety in the online medical community are divided into four types: fear type, anger type, depression type and disgust type according to their emotions. The specific analysis of the salient features of each type of user portrait provides a more comprehensive explanation of the type of user portrait labels for the phenomenon of health anxiety in the online medical community.

Key words： online health community; health anxiety; user portrait; K-means

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