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数据分析与知识发现  2019, Vol. 3 Issue (4): 22-32    DOI: 10.11925/infotech.2096-3467.2018.1153
  专题 本期目录 | 过刊浏览 | 高级检索 |
中文网络健康社区中的用户信息需求挖掘研究*——以求医网肿瘤板块数据为例
陆泉1,2,朱安琪1,张霁月1,陈静3()
1武汉大学信息管理学院 武汉 430072
2武汉大学大数据研究院 武汉 430072
3华中师范大学信息管理学院 武汉 430079
Research on User Information Requirement in Chinese Network Health Community: Taking Tumor-forum Data of Qiuyi as an Example
Quan Lu1,2,Anqi Zhu1,Jiyue Zhang1,Jing Chen3()
1School of Information Management, Wuhan University, Wuhan 430072, China
2Big Data Research Institute, Wuhan University, Wuhan 430072, China
3School of Information Management, Central China Normal University, Wuhan 430079, China
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摘要 

【目的】构建适应大数据环境的中文网络健康社区用户信息需求挖掘框架, 以肿瘤科为例分析用户信息需求。【方法】使用潜在语义索引(LSI)模型和MapReduce分布式文本聚类技术对中文网络健康社区——求医网肿瘤板块的全部提问数据(共计24 305条)进行用户信息需求挖掘。【结果】挖掘出用户的5个信息需求类目及其占比: 治疗(43.3%)、病理及病因(34.5%)、检查(12.1%)、术后(7.0%)、预防(3.1%), 各类目下Top20关键词; 发现国内外各需求类目占比差异巨大; 预防信息需求将持续上升; 需求的性别差异显著, 男性最关注治疗信息、女性最关注病理及病因信息; 需求的年龄差异较大, 青年群体占比极高(83.79%)等。【局限】可能存在更好的阈值选择, 更完整医学主题词表; 尚未进行信息需求的多维分析。【结论】本文框架可在大数据环境下挖掘用户信息需求, 并分析需求的变化趋势以及年龄与性别差异。

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陆泉
朱安琪
张霁月
陈静
关键词 网络健康社区信息需求大数据挖掘分布式文本聚类肿瘤    
Abstract

[Objective] This paper constructs an information demand mining framework of Chinese online health community users adapted to the big data environment, and analyzes the user information needs by taking the data of tumor-forum as an example. [Methods] The Latent Semantic Indexing (LSI) model and MapReduce distributed text clustering technology were used in this framework to mine the user information needs. We use all the Q&A data (24,305 in total) from tumor-forum of Chinese online health community (qiuyi.cn) as the experimental data source. [Results] The proposed framework mines the five information needs and their proportions of the tumor users: treatment (43.3%), pathology and etiology (34.5%), examination (12.1%), postoperative (7.0%), prevention (3.1%), and top 20 keywords of these needs. The analysis shows the growth of each needs, and the significant difference between domestic users and foreign users. Gender differences are also significant, the male need treatment information most, while female need pathological and etiological information most. Age difference is large too, and the information needs of young people are the largest (83.79%), etc. [Limitations] There may be better threshold selection, and the medical thesaurus is not prefect. The analysis of information needs is not multidimensional. [Conclusions] The proposed framework is feasible. The paper found the trend of the demand distribution changes with year and the distribution of users information needs vary with age or gender.

Key wordsOnline Health Community    Information Needs    Big Data Mining    Distributed Text Clustering    Tumor
收稿日期: 2018-10-18     
基金资助:*本文系教育部人文社会科学重点研究基地重大项目“大数据资源的挖掘与服务研究——面向医疗健康领域”(项目编号: 17JJD870002)和教育部规划基金项目“基于前景理论的信息搜索过程建模与预测研究”(项目编号: 18YJA870002)的研究成果之一
引用本文:   
陆泉,朱安琪,张霁月,陈静. 中文网络健康社区中的用户信息需求挖掘研究*——以求医网肿瘤板块数据为例[J]. 数据分析与知识发现, 2019, 3(4): 22-32.
Quan Lu,Anqi Zhu,Jiyue Zhang,Jing Chen. Research on User Information Requirement in Chinese Network Health Community: Taking Tumor-forum Data of Qiuyi as an Example. Data Analysis and Knowledge Discovery, DOI:10.11925/infotech.2096-3467.2018.1153.
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http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.1153
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