|
|
|
| Study of User Information Requirements in an Online Health Community Based on the Distribution of User Profile and Theme Features: Taking Colorectal Cancer Data from YiXiang as an Example |
| Sheng Shu1, Huang Qi2, Zheng Shuya1, Yang Yang1, Xie Qiwen1, Zhang Ge3, Qin Xinguo4 |
1.School of Information Management, Nanjing University, Nanjing 210046
2.Nanjing Research Based of National Information Management, Nanjing University, Nanjing 210093
3.School of Management & Engineering, Nanjing University, Nanjing 210093
4.Information Office of Nanjing Audit University, Nanjing 211815 |
|
|
|
|
Abstract In this study, the user information requirements of an online health community were constructed based on user profiles and theme feature extraction to reveal user behavioral rules and characteristics of different types of user groups in different roles, providing the basis for promoting the development of an online health community. Python was used to obtain user data from the colorectal cancer circle of the YiXiang community. Future typical user identification metrics were constructed taking three characteristics into account: user role attributes, user behavior attributes, and text features. These three characteristics were combined with a theme classification system to build the user profile concept model. The user group was divided into four categories, and the user behavior recognition algorithm and theme clustering algorithm were used to identify each user’s interest content in different roles and obtain an accurate picture of the user requirements. By mining and analyzing differences in gender, age, and theme distribution among the four types of user roles, we found that there are significant differences in information requirements among different user groups.
|
|
Received: 19 December 2019
|
|
|
|
1 吴江, 刘冠君, 胡仙. 在线医疗健康研究的系统综述: 研究热点、主题演化和研究方法[J]. 数据分析与知识发现, 2019, 3(4): 2-12.
2 张海涛, 崔阳, 王丹, 等. 基于概念格的在线健康社区用户画像研究[J]. 情报学报, 2018, 37(9): 912-922.
3 李敏, 黄成, 叶孟良, 等. 微博环境下用户健康信息扩散行为影响因素实证研究[J]. 图书馆学研究, 2017(21): 81-91.
4 张敏, 车雨霏, 张艳. 双渠道视角下在线健康社区用户诊疗信息求助行为的形成路径分析[J]. 情报科学, 2019, 37(2): 25-32,101.
5 李月琳, 张秀, 王姗姗. 社交媒体健康信息质量研究: 基于真伪健康信息特征的分析[J]. 情报学报, 2018, 37(3): 294-304.
6 钱明辉, 徐志轩, 王珊. 基于用户参与的在线健康平台信息服务质量研究[J]. 情报学报, 2019, 38(2): 132-142.
7 Pérez-Pérez M, Pérez-Rodríguez G, Fdez-Riverola F, et al. Using Twitter to understand the human bowel disease community: exploratory analysis of key topics[J]. Journal of Medical Internet Research, 2019, 21(8): e12610.
8 Zhang Y, He D, Sang Y. Facebook as a platform for health information and communication: a case study of a diabetes group[J]. Journal of Medical Systems, 2013, 37(3): 9942.
9 Sudau F, Friede T, Grabowski J, et al. Sources of information and behavioral patterns in online health forums: observational study[J]. Journal of Medical Internet Research, 2014, 16(1): e10.
10 刘冰, 历鑫, 张赫钊, 等. 网络健康社区中身份转换期女性信息需求主题特征及情感因素研究——以“妈妈网”中“备孕版块”为例[J]. 情报理论与实践, 2019, 42(5): 87-92.
11 Kwak H, Lee C, Park H, et al. What is Twitter, a social network or news media?[C]// Proceedings of the 19th International Conference on World Wide Web. New York: ACM Press, 2010: 591-600.
12 Rodgers S, Chen Q M. Internet community group participation: psychosocial benefits for women with breast cancer[J]. Journal of Computer-Mediated Communication, 2005, 10(4): JCMC1047.
13 吴江, 赵颖慧, 高嘉慧. 医疗舆情事件的微博意见领袖识别与分析研究[J]. 数据分析与知识发现, 2019, 3(4): 53-62.
14 陈东华, 张润彤, 付磊, 等. SNOMED CT体系下医疗健康大数据映射和迁移方法研究[J]. 情报学报, 2018, 37(5): 524-532.
15 陆泉, 朱安琪, 张霁月, 等. 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.
16 金碧漪, 许鑫. 网络健康社区中的主题特征研究[J]. 图书情报工作, 2015, 59(12): 100-105.
17 吴江, 周露莎. 在线医疗社区中知识共享网络及知识互动行为研究[J]. 情报科学, 2017, 35(3): 144-151.
18 Park A, Conway M, Chen A T. Examining thematic similarity, difference, and membership in three online mental health communities from reddit: a text mining and visualization approach[J]. Computers in Human Behavior, 2018, 78: 98-112.
19 Gao Y, Gu L, Wang Y F, et al. Constructing a Chinese electronic medical record corpus for named entity recognition on resident admit notes[J]. BMC Medical Informatics and Decision Making, 2019, 19(2): 67-78. |
|
|
|
