Predicting users' continued engagement in online health communities from the quantity and quality of received support

被引：21
作者：
Wang, Xiangyu [1 ]
High, Andrew [2 ]
Wang, Xi [3 ]
Zhao, Kang [4 ]
机构：
[1] Univ Iowa, Interdisciplinary Grad Program Informat, Iowa City, IA 52242 USA
[2] Penn State Univ, Dept Commun Arts & Sci, University Pk, PA 16802 USA
[3] Cent Univ Finance & Econ, Sch Informat, Beijing, Peoples R China
[4] Univ Iowa, Tipple Coll Business, Iowa City, IA 52242 USA
来源：
JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY | 2021年 / 72卷 / 06期
关键词：
SOCIAL SUPPORT; BREAST-CANCER; WOMEN; CONSEQUENCES; INFORMATION; EXPRESSION; MISMATCHES; RECEPTION; MODERATOR; SURVIVAL;
D O I：
10.1002/asi.24436
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Online health communities (OHCs) have been major resources for people with similar health concerns to interact with each other. They offer easily accessible platforms for users to seek, receive, and provide supports by posting. Taking the advantage of text mining and machine learning techniques, we identified social support type(s) in each post and a new user's support needs in an OHC. We examined a user's first-time support-seeking experience by measuring both quantity and quality of received support. Our results revealed that the amount and match of received support are positive and significant predictors of new users' continued engagement. Our outcomes can provide insight for designing and managing a sustainable OHC by retaining users.
引用
收藏
页码：710 / 722
页数：13
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