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数据分析与知识发现  2019, Vol. 3 Issue (12): 10-20    DOI: 10.11925/infotech.2096-3467.2018.1427
     研究论文 本期目录 | 过刊浏览 | 高级检索 |
突发事件情境下在线健康社区用户交互行为研究 *
胡哲1,2,查先进1(),严亚兰3
1 武汉大学信息管理学院 武汉 430072
2 武汉大学图书情报国家级实验教学示范中心 武汉 430072
3 武汉科技大学恒大管理学院 武汉 430065
Interactive Behaviors of Online Health Community Users in Emergency
Zhe Hu1,2,Xianjin Zha1(),Yalan Yan3
1 School of Information Management, Wuhan University, Wuhan 430072, China
2 Laboratory Center for Library and Information Science, Wuhan University, Wuhan 430072, China
3 Evergrande School of Management, Wuhan University of Science and Technology, Wuhan 430065, China
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摘要 

【目的】探索突发事件情境下用户交互行为的规律。【方法】构建基于发帖和回帖的有向赋值矩阵(交互网络), 展现交互网络的整体网络结构图, 对交互网络进行小世界分析, 对中心性指标、结构洞指标与用户交互行为分别进行相关分析。【结果】整体网络结构表现出小世界效应, 特征向量中心性与发帖行为之间具有较大的正相关, 点度中心性与回帖行为之间具有较大的正相关, 结构洞与回帖行为之间具有较大的正相关。【局限】数据类型比较单一。【结论】本研究成果为推动在线健康社区的健康和可持续发展提供了参考和指导。

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胡哲
查先进
严亚兰
关键词 中心性结构洞在线健康社区突发事件交互行为    
Abstract

[Objective] This study explores the interactive behaviors of online health community users in emergency. [Methods] Firstly, we constructed a directed matrix based on the posts and replies and illustrated the structure of this interactive network. Then, we conducted a small-world, and correlation analysis for the centrality index, structural hole index and user interactive behaviors respectively. [Results] The whole network had a small-world effect. The eigenvector centrality had significant positive correlation with the number of posts and the degree centrality had significant positive correlation with the number of replies. Meanwhile, structural holes had significant positive correlations with the number of replies. [Limitations] The data types were not diversified. [Conclusions] This study provides useful references and guidelines for the development of online health communities.

Key wordsCentrality    Structural Holes    Online Health Communities    Emergency    Interactive Behaviors
收稿日期: 2018-12-18     
中图分类号:  G350  
基金资助:*本文系国家自然科学基金面上项目“认知转变和IT社会结构视角下互联网用户适应性信息行为影响规律及优化研究”(项目编号: 71573195);国家自然科学基金面上项目“交流可见度和结构洞视角下交互记忆系统及知识行为研究”(项目编号: 71774126);国家自然科学基金面上项目“社会连接和认知负荷视角下网络用户从众信息行为研究”(项目编号: 71874124)
通讯作者: 查先进     E-mail: xianjinzha@163.com
引用本文:   
胡哲,查先进,严亚兰. 突发事件情境下在线健康社区用户交互行为研究 *[J]. 数据分析与知识发现, 2019, 3(12): 10-20.
Zhe Hu,Xianjin Zha,Yalan Yan. Interactive Behaviors of Online Health Community Users in Emergency. Data Analysis and Knowledge Discovery, DOI:10.11925/infotech.2096-3467.2018.1427.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.1427
图1  “疫苗吧”2018年1月至9月主题帖数
用户 A B C D E
A 0 2 4 1 2
B 4 0 1 0 3
C 0 2 0 2 4
D 1 4 4 0 1
E 2 3 5 2 0
表1  用户关系示例矩阵
图2  整体网络结构
用户名 入度 出度 点度中心性
剿匪总司令部 190 6 196
glj花自飘零 170 0 170
幼稚完阿峯 111 44 155
下辈子韩国 92 32 124
liuxingyu607 114 0 114
水果糖T_T 37 59 96
安医生 81 5 86
表2  用户点度中心性排名(部分)
对比项 样本网络 随机网络
节点数 2 340 2 340
边数 5 583 5 604
平均聚类系数 0.093 0.001
平均路径长度 4.29 6.072
表3  样本网络与随机网络的网络参数对比
指标或行为 最小值 最大值 均值 中位数 标准差
中心性
指标
点度
中心性
0 196 4.772 1 11.495
中介
中心性
0 116 689.52 606.043 0 4 597.974
接近
中心性
0 1 0.489 0.290 0.386
特征向
量中心性
0 1 0.016 0 0.055
结构洞
指标
有效规模 0 191.06 4.512 1 10.930
效率 0 1 0.970 1 0.088
约束 0.01 1.84 0.714 1 0.359
层级 0 1 0.584 1 0.466
交互
行为
发帖数 0 62 0.435 0 1.922
回帖数 0 63 2.386 1 4.688
表4  基本统计数据
网络指标 交互行为
发帖数 回帖数
中心性指标 点度中心性 0.495** 0.605**
中介中心性 0.493** 0.565**
接近中心性 -0.151** -0.082**
特征向量中心性 0.554** 0.313**
结构洞指标 有效规模 0.484** 0.580**
效率 -0.058** -0.147**
约束 -0.282** -0.422**
层级 -0.173** -0.279**
表5  网络指标与用户交互行为的相关系数
[1] Maloney-Krichmar D, Preece J . A Multilevel Analysis of Sociability, Usability, and Community Dynamics in an Online Health Community[J]. ACM Transactions on Computer-Human Interaction, 2005,12(2):201-232.
[2] 张静, 郭伟, 王磊 , 等. 用户交互对产品知识认知扩散的影响及社区激励研究[J]. 科技进步与对策, 2018,35(16):7-15.
( Zhang Jing, Guo Wei, Wang Lei , et al. The Influence of User Interaction on the Cognition and Diffusion of Products Knowledge and Community Incentive Research[J]. Science & Technology Progress and Policy, 2018,35(16):7-15.)
[3] Khobzi H, Lau R Y K, Cheung T C H . The Outcome of Online Social Interactions on Facebook Pages: A Study of User Engagement Behavior[J]. Internet Research, 2019,29(1):2-23.
[4] Fang J M, Li J, Prybutok V R . Posting-Related Attributes Driving Differential Engagement Behaviors in Online Travel Communities[J]. Telematics and Informatics, 2018,35(5):1263-1276.
[5] 项惠惠 . 在线社交媒体中用户信息传播行为预测研究[D]. 南京: 南京邮电大学, 2018.
( Xiang Huihui . Research on the Prediction of Users’ Information Transmission Behavior in Online Social Networks: Taking Micro-blog Users as an Example[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2018.)
[6] Wang C, Li Q, Wang L , et al. Incorporating Message Embedding into Co-factor Matrix Factorization for Retweeting Prediction [C]//Proceedings of the 2017 International Joint Conference on Neural Networks. IEEE, 2017: 1265-1272.
[7] 齐云飞, 张玥, 朱庆华 . 信息生态链视角下社会化问答用户的信息交互行为研究[J]. 情报理论与实践, 2018,41(12):1-7, 26.
( Qi Yunfei, Zhang Yue, Zhu Qinghua . Information Interaction Behavior of Users in Social Q&A Community from the Perspective of Information Ecology Chain[J]. Information Studies: Theory & Application, 2018,41(12):1-7, 26.)
[8] 邓胜利 . 网络用户信息交互行为研究模型[J]. 情报理论与实践, 2015,38(12):53-56, 87.
( Deng Shengli . Research Model of Network User Information Interaction Behavior[J]. Information Studies: Theory & Application, 2015,38(12):53-56, 87.)
[9] 王雪芬, 朱庆华, 常李艳 , 等. 虚拟求职社区的用户交互行为特点——以应届生求职论坛为例[J]. 图书情报工作, 2018,62(10):62-69.
( Wang Xuefen, Zhu Qinghua, Chang Liyan , et al. Characteristics of User Interaction Behaviors in Virtual Job-hunting Communities——Taking Ying Jie Sheng BBS as an Example[J]. Library and Information Service, 2018,62(10):62-69.)
[10] Bonchi F, Castillo C, Gionis A , et al. Social Network Analysis and Mining for Business Applications[J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): Article No. 22.
[11] Nepal S, Paris C, Pour P A , et al. Interaction-Based Recommendations for Online Communities[J]. ACM Transactions on Internet Technology, 2015,15(2):14-24.
[12] 王晰巍, 韦雅楠, 邢云菲 , 等. 新媒体环境下企业与用户信息交互行为模型及特征研究[J]. 图书情报工作, 2018,62(18):6-15.
( Wang Xiwei, Wei Ya’nan, Xing Yunfei , et al. Research on Enterprises and Users Information Interaction Behavior Model and Characteristics in New Media Environment[J]. Library and Information Service, 2018,62(18):6-15.)
[13] 余重远, 李珊, 赵文 , 等. 基于SNA的网络舆论领袖确定方法研究[J]. 科技信息, 2011(12):36-37.
( Yu Chongyuan, Li Shan, Zhao Wen , et al. Research on Determining Methods of Internet Opinion Leader Based on SNA[J]. Science, 2011(12):36-37.)
[14] 张继东, 杨杨 . 基于交互行为和情感倾向的移动社交网络用户影响力度量模型研究[J]. 情报理论与实践, 2019,42(1):112-117, 93.
( Zhang Jidong, Yang Yang . Measurement Model of Mobile Social Network Users’ Influence Based on Interactive Behavior and Emotional Tendency[J]. Information Studies: Theory & Application, 2019,42(1):112-117, 93.)
[15] 张凤娟, 王濛, 周刚 . 基于活动网络的微博用户影响力分析[J]. 计算机技术与发展, 2018,28(9):162-167, 171.
( Zhang Fengjuan, Wang Meng, Zhou Gang . Analysis of User Influence in Microblog Based on Activity Network[J]. Computer Technology and Development, 2018,28(9):162-167, 171.)
[16] 陈芬, 付希, 何源 , 等. 融合社会网络分析与影响力扩散模型的微博意见领袖发现研究[J]. 数据分析与知识发现, 2018,2(12):60-67.
( Chen Fen, Fu Xi, He Yuan , et al. Identifying Weibo Opinion Leaders with Social Network Analysis and Influence Diffusion Model[J]. Data Analysis and Knowledge Discovery, 2018,2(12):60-67.)
[17] 王忠义, 张鹤铭, 黄京 , 等. 基于社会网络分析的网络问答社区知识传播研究[J]. 数据分析与知识发现, 2018,2(11):80-94.
( Wang Zhongyi, Zhang Heming, Huang Jing , et al. Studying Knowledge Dissemination of Online Q&A Community with Social Network Analysis[J]. Data Analysis and Knowledge Discovery, 2018,2(11):80-94. )
[18] Van Der Eijk M, Faber M J, Aarts J W M , et al. Using Online Health Communities to Deliver Patient-Centered Care to People with Chronic Conditions[J]. Journal of Medical Internet Research, 2013,15(6):190-200.
[19] 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.
( Jin Biyi, Xu Xin . Research on Theme Features in Online Health Community[J]. Library and Information Service, 2015,59(12):100-105.)
[20] 于皓 . 脑血管病医疗论坛的主题与文本情感分析研究[D]. 北京: 北京交通大学, 2016.
( Yu Hao . Analysis on the Subject and Emotion of the Medical Forum of Cerebrovascular Disease[D]. Beijing: Beijing Jiaotong University, 2016.)
[21] 张星, 陈星, 侯德林 . 在线健康信息披露意愿的影响因素研究: 一个集成计划行为理论与隐私计算的模型[J]. 情报资料工作, 2016,37(1):48-53.
( Zhang Xing, Chen Xing, Hou Delin . An Analysis of Online Health Information Disclosure Willingness Influencing Factors: An Integrated Model of TPB and Privacy Calculus[J]. Information and Documentation Services, 2016,37(1):48-53.)
[22] 王文韬, 谢阳群, 刘坤锋 . 基于扎根理论的虚拟健康社区用户使用意愿研究[J]. 情报资料工作, 2017,38(3):75-82.
( Wang Wentao, Xie Yangqun, Liu Kunfeng . Research on Virtual Health Community Users Willingness Based on Grounded Theory[J]. Information and Documentation Services, 2017,38(3):75-82.)
[23] 杨化龙, 鞠晓峰 . 社会支持与个人目标对健康状况的影响[J]. 管理科学, 2017,30(1):53-61.
( Yang Hualong, Ju Xiaofeng . The Effects of Social Support and Individual Goal on Health Condition[J]. Journal of Management Science, 2017,30(1):53-61.)
[24] 吕英杰 . 网络健康社区中的文本挖掘方法研究[D]. 上海: 上海交通大学, 2013.
( Lv Yingjie . Research on Text Mining in Online Health Community[D]. Shanghai: Shanghai Jiao Tong University, 2013.)
[25] Oh S . The Characteristics and Motivations of Health Answers for Sharing Information, Knowledge, and Experiences in Online Environments[J]. Journal of the American Society for Information Science and Technology, 2012,63(3):543-557.
[26] 侯贵生, 王鹏民, 杨磊 . 在线健康社区用户知识转化与共享的演化博弈分析[J]. 情报科学, 2017,35(7):31-38.
( Hou Guisheng, Wang Pengmin, Yang Lei . Research on Evolutionary Game of the Knowledge Conversion and Sharing of Online Health Community Users[J]. Information Science, 2017,35(7):31-38.)
[27] Chen C J, Hung S W . To Give or to Receive? Factors Influencing Members’ Knowledge Sharing and Community Promotion in Professional Virtual Communities[J]. Information & Management, 2010,47(4):226-236.
[28] 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.
( Wu Jiang, Zhou Lusha . The Study of Knowledge Sharing Network and Users’ Knowledge Interaction in Online Health Community[J]. Information Science, 2017,35(3):144-151.)
[29] 王舒瑶 . 网络社区健康传播的网络特征及社会支持研究——以豆瓣网“我们都有一口烂牙”小组为例[D]. 上海: 上海交通大学, 2015.
( Wang Shuyao . Network Characters and Social Support in Network Community for Health Communication[D]. Shanghai: Shanghai Jiao Tong University, 2015.)
[30] Centola D . The Spread of Behavior in an Online Social Network Experiment[J]. Science, 2010,329(5996):1194-1197.
[31] Ba S, Wang L . Digital Health Communities: The Effect of Their Motivation Mechanisms[J]. Decision Support Systems, 2013,55(4):941-947.
[32] 刘璇, 汪林威, 李嘉 , 等. 在线健康社区中用户回帖行为影响机理研究[J]. 管理科学, 2017,30(1):62-72.
( Liu Xuan, Wang Linwei, Li Jia , et al. Research on Mechanisms of User Replying Behaviors in Online Health Communities[J]. Journal of Management Science, 2017,30(1):62-72.)
[33] Wang X, Zhao K . Social Support and User Engagement in Online Health Communities [C]//Proceedings of the 2014 International Conference for Smart Health (ICSH). Springer International Publishing, 2014: 97-110.
[34] 翟羽佳, 张鑫, 王芳 . 在线健康社区中的用户参与行为——以“百度戒烟吧”为例[J]. 图书情报工作, 2017,61(7):75-82.
( Zhai Yujia, Zhang Xin, Wang Fang . User Engagement in Online Health Communities——Taking Baidu Quitting Smoking Post Bar as an Example[J]. Library and Information Service, 2017,61(7):75-82.)
[35] 王国华, 刘菊, 杨腾飞 , 等. 网络空间中艾滋病的社会支持研究——以百度贴吧“HIV 吧”为例[J]. 情报杂志, 2015,34(11):105-110.
( Wang Guohua, Liu Ju, Yang Tengfei , et al. Social Support for AIDS in Cyberspace -Take Baidu Post Bar “HIV Bar” for Example[J]. Journal of Intelligence, 2015,34(11):105-110.)
[36] Milgram S . The Small World Problem[J]. Psychology Today, 1967,2(1):60-67.
[37] Watts D J, Strogatz S H . Collective Dynamics of “Small World” Networks[J]. Nature, 1998,393(6684):440-442.
[38] Newman M E J, Watts D J . Renorm Alization Group Analysis of the Small-world Network Model[J]. Physics Letters A, 1999,263(4):341-346.
[39] 曲少玲, 胡德华 . 小世界理论研究的可视化分析[J]. 图书馆杂志, 2016,35(6):57-65.
( Qu Shaoling, Hu Dehua . A Visual Analysis on the Research of Small World Theory[J]. Library Journal, 2016,35(6):57-65.)
[40] Capaldo A, Giannoccaro I . Interdependence and Network-level Trust in Supply Chain Networks: A Computational Study[J]. Industrial Marketing Management, 2015,44(1):180-195.
[41] Wandelt S, Sun X Q, Zanin M , et al. QRE: Quick Robustness Estimation for Large Complex Networks[J]. Future Generation Computer Systems, 2018,83:413-424.
[42] Centola D . The Social Origins of Networks and Diffusion[J]. American Journal of Sociology, 2015,120(5):1295-1338.
[43] Pereira J B, Westman E, Stomrud E , et al. Abnormal Structural Brain Connectome in Individuals with Preclinical Alzheimer’s Disease[J]. Cerebral Cortex, 2018,28(10):3638-3649.
[44] Lee M H, Kim D Y, Chung M K , et al. Topological Properties of the Structural Brain Network in Autism via Epsilon- neighbor Method[J]. IEEE Transactions on Biomedical Engineering, 2018,65(10):2323-2333.
[45] Lee J, Kim S . Exploring the Role of Social Networks in Affective Organizational Commitment: Network Centrality, Strength of Ties, and Structural Holes[J]. American Review of Public Administration, 2011,41(2):205-223.
[46] Wang C L, Rodan S, Fruin M , et al. Knowledge Networks, Collaboration Networks, and Exploratory Innovation[J]. Academy of Management Journal, 2014,57(2):484-514.
[47] Burt R S . Structural Holes: The Social Structure of Competition[M]. Cambridge, MA: Harvard University Press, 1992.
[48] Burt R S . Structural Holes and Good Ideas[J]. American Journal of Sociology, 2004,110(2):349-399.
[49] Burt R S . Reinforced Structural Holes[J]. Social Networks, 2015,43:149-161.
[50] 刘军 . 社会网络分析导论[M]. 北京: 社会科学文献出版社, 2004.
( Liu Jun . An Introduction to Social Network Analysis[M]. Beijing: Social Science Academic Press, 2004.)
[51] Freeman L C . Centrality in Social Networks: Conceptual Clarification[J]. Social Networks, 1979,1(3):215-239.
[52] Brandes U, Borgatti S P, Freeman L C . Maintaining the Duality of Closeness and Betweenness Centrality[J]. Social Networks, 2016,44:153-159.
[53] Bonacich P, Lloyd P . Eigenvector Centrality and Structural Zeroes and Ones: When is a Neighbor Not a Neighbor[J]. Social Networks, 2015,43:86-90.
[54] 姜鑫, 田志伟 . 微博社区内信息传播的“小世界”现象及实证研究——以腾讯微博为例[J]. 情报科学, 2012,30(8):1139-1142.
( Jiang Xin, Tian Zhiwei . An Empirical Study on Information Dissemination in Microblog Community from the Perspective of Small-world Property——A Case Study with Tencent Microblog[J]. Information Science, 2012,30(8):1139-1142.)
[55] 关鹏, 王曰芬, 曹嘉君 . 整合主题的学科知识网络构建与演化分析框架研究[J]. 情报科学, 2018,36(9):3-8.
( Guan Peng, Wang Yuefen, Cao Jiajun . Research on Framework of Construction and Evolution Analysis of Discipline Knowledge Network Based on Integrating Topic[J]. Information Science, 2018,36(9):3-8.)
[56] Figueiredo C, Chen W H, Azevedo J . Central Nodes and Surprise in Content Selection in Social Networks[J]. Computers in Human Behavior, 2015,51:382-392.
[57] Ballinger G A, Cross R, Holtom B C . The Right Friends in the Right Places: Understanding Network Structure as a Predictor of Voluntary Turnover[J]. Journal of Applied Psychology, 2016,101(4):535-548.
[58] Tortoriello M . The Social Underpinnings of Absorptive Capacity: The Moderating Effects of Structural Holes on Innovation Generation Based on External Knowledge[J]. Strategic Management Journal, 2015,36(4):586-597.
[59] Liao Y C, Phan P H . Internal Capabilities, External Structural Holes Network Positions, and Knowledge Creation[J]. Journal of Technology Transfer, 2016,41(5):1148-1167.
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