在线医疗社区健康焦虑用户关联数据挖掘与群体画像分类研究
User Association Data Mining and Group Portrait Classification of Health Anxiety in Online Medical Community
张艳丰 1高靖超 1洪闯1
作者信息
- 1. 湘潭大学公共管理学院,湖南湘潭 411105
- 折叠
摘要
[目的/意义]通过构建在线医疗社区健康焦虑用户画像,以揭示不同类型健康焦虑用户群体需求、行为等特征表现,为实现用户健康信息精准推荐和个性化服务提供一定依据.[方法/过程]根据已有研究成果并结合在线医疗社区健康焦虑用户数据特征构建标签框架,通过形式概念分析方法构建健康焦虑用户标签概念格,挖掘用户特征关联规则并实现健康焦虑用户群体分类.[结果/结论]依据不同类型健康焦虑用户群体标签特征,将其划分为过度询证型、积极求解型、消极逃避型以及边缘划水型4类,具体分析各类健康焦虑用户画像的关键特征并提出相应的引导策略.[创新/局限]本研究揭示了不同健康焦虑用户群体的特征表现与关联规则,对引导健康焦虑用户树立正确健康观及促进在线医疗社区可持续发展具有一定指导意义.但未考虑不同疾病社区多源数据融合的情况,样本涵盖的广度与深度有待进一步完善.
Abstract
[Purpose/significance]By constructing a portrait of health anxiety users in the online medical community,we can reveal the characteristics of needs and behaviors of different types of health anxiety users,and provide a basis for accurate recommendation and personalized service of user health information.[Method/process]According to the existing research results and the characteris-tics of health anxiety user data in online medical community,the label framework is constructed.The concept lattice of health anxiety user label is constructed by formal concept analysis method,and the association rules of user characteristics are mined to realize the classification of health anxiety user groups.[Result/conclusion]According to the label characteristics of different types of health anxi-ety user groups,they are divided into four categories:over-inquiry type,positive solution type,negative escape type and edge stroke type.The key characteristics of various types of health anxiety user portraits are analyzed in detail and a more comprehensive explana-tion is provided.[Innovation/limitation]This study reveals the characteristics and association rules of different health anxiety user groups,which has certain guiding significance for guiding health anxiety users to establish a correct view of health and promoting the sustainable development of online medical community.However,the multi-source data fusion of different disease communities is not considered,and the breadth and depth of the sample coverage need to be further improved.
关键词
在线医疗社区/健康焦虑/用户画像/用户标签/关联规则Key words
online health community/health anxiety/user portrait/user tags/association rules引用本文复制引用
出版年
2024
NETL
NSTL
万方数据