情报研究

基于指数随机图模型的在线健康社区多元主体交互影响机制研究

  • 冯翠翠 ,
  • 易明 ,
  • 莫富传
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  • 1 华中师范大学信息管理学院, 武汉 430079;
    2 华中师范大学中国图书馆创新发展研究中心, 武汉 430079;
    3 武汉大学信息管理学院, 武汉 430072
冯翠翠,博士研究生;易明,教授,博士,博士生导师,通信作者,E-mail:yiming0415@ccnu.edu.cn;莫富传,博士研究生。

收稿日期: 2023-08-14

  修回日期: 2023-11-23

  网络出版日期: 2024-04-24

基金资助

本文系国家社会科学基金重点项目"在线健康社区知识共创机理及引导机制研究"(项目编号:21ATQ006)研究成果之一。

Research on the Influencing Mechanism of Multi-subject Interaction in Online Health Community Based on ERGM

  • Feng Cuicui ,
  • Yi Ming ,
  • Mo Fuchuan
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  • 1 School of Information Management, Central China Normal University, Wuhan 430079;
    2 China Library Innovation and Development Research Center, Central China Normal University, Wuhan 430079;
    3 School of Information Management, Wuhan University, Wuhan 430072

Received date: 2023-08-14

  Revised date: 2023-11-23

  Online published: 2024-04-24

Supported by

This work is supported by Natural Science Foundation of China titled "Research on Knowledge Co-creation Mechanism and Guidance Mechanism of Online Health Community" (Grant No. 21ATQ006).

摘要

[目的/意义]通过分析在线健康社区多元主体交互行为的多维影响因素及其作用机理,揭示在线健康社区多元主体交互的特征规律和交互网络的社会化形成过程。[方法/过程]采集淋巴瘤之家的交互数据集,构建在线健康社区多元主体交互网络,并借助指数随机图模型分析主体类型、主体同质属性、社会资本属性以及网络局部结构变量对多元主体交互的影响。[结果/结论]跨主体交互是多元主体间的主要交互模式,患者、疑似患者、医生、家属、朋友、社区管理员和健康焦虑用户均倾向于与其他类型的用户进行交互;基于疾病类型与地理位置的同质效应、健康状态异质的"知识差"效应是促进交互的重要条件;与活跃度相关的发出效应和接收效应显著,基于好友关系的"熟人效应"也比较突出;多元主体交互网络的形成过程也具有显著的互惠性和传递性特征。

本文引用格式

冯翠翠 , 易明 , 莫富传 . 基于指数随机图模型的在线健康社区多元主体交互影响机制研究[J]. 图书情报工作, 2024 , 68(7) : 88 -101 . DOI: 10.13266/j.issn.0252-3116.2024.07.009

Abstract

[Purpose/Significance] By analyzing the multifaceted influencing factors and their mechanisms of multi-subject interactions in online health communities (OHCs), this study aims to unveil the characteristics of multi-subject interaction and the socialization process of the networks in OHCs.[Method/Process] With the interaction data collected from the Lymphoma House, this study constructed a multi-subject interactions network among the community participants, and utilized the exponential random graph model (ERGM) to analyze the influences of subject types, subject homophily attributes, social capital attributes, and network local structural variables on diverse interactions among participants.[Result/Conclusion] Cross-subject interactions emerge as the primary mode of interaction among multi-subject. Patients, suspected patients, doctors, family members, friends, OHCs administrators and health-anxious users tend to interact with other types of users. Homophily based on disease type and geographic location, as well as the "knowledge gap" effect caused by heterogeneous health status, are important conditions that promote interactions. Effects related to activity levels, both in sending and receiving interactions, are significant, and the "acquaintance effect" based on friend relationships is also prominent. The formation process of the diverse interactions network also exhibits significant reciprocity and transitivity features.

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