图书情报工作 ›› 2022, Vol. 66 ›› Issue (12): 68-81.DOI: 10.13266/j.issn.0252-3116.2022.12.007

• 情报研究 • 上一篇    下一篇

突发传染病情境下社会化问答平台用户角色形成及转变——以知乎平台为例

陈苗苗1, 安璐1,2   

  1. 1. 武汉大学信息管理学院 武汉 430072;
    2. 武汉大学信息资源研究中心 武汉 430072
  • 收稿日期:2021-12-30 修回日期:2022-04-06 出版日期:2022-06-20 发布日期:2022-06-25
  • 通讯作者: 安璐,数据管理与知识服务研究室主任,教授,博士,博士生导师,通信作者,E-mail:anlu97@163.com
  • 作者简介:陈苗苗,博士研究生。
  • 基金资助:
    本文系国家自然科学基金面上项目"危机情境下网络信息传播失序识别与干预方法研究"(项目编号:72174153)、国家自然科学基金创新研究群体项目"信息资源管理"(项目编号:71921002)和国家自然科学基金重大课题"国家安全大数据综合信息集成与分析方法"(项目编号:71790612)研究成果之一。

User Role Formation and Transformation of Socialized Q&A Platforms in the Context of Infectious Disease Outbreaks:Taking the Zhihu Platform as an Example

Chen Miaomiao1, An Lu1,2   

  1. 1. School of Information Management, Wuhan University, Wuhan 430072;
    2. Center for Studies of Information Resources, Wuhan University, Wuhan 430072
  • Received:2021-12-30 Revised:2022-04-06 Online:2022-06-20 Published:2022-06-25

摘要: [目的/意义]探究突发传染病情境下问答平台用户角色分类方法、角色形成关键因素及转变特点和差异。[方法/过程]收集问答平台Covid-19疫情数据相关数据共计702 927条,从参与程度和价值维度识别用户角色,基于信息人因子、信息因子和信息环境因子识别社区用户角色形成的影响因素,结合多分类模型和SHapley Additive exPlanations (SHAP)模型分析影响不同角色形成的关键因素,利用FP-growth关联规则算法挖掘不同角色转变下的行为模式和主题特点。[结果/结论]研究结果表明用户倾向于维持角色不变且转变方向以积极型和潜水型为主,信息量是不同角色形成的关键因素,不同转变阶段的用户角色转变特征变化程度及所有转变阶段的用户角色转变行为具有显著差异。

关键词: 用户角色, 知乎问答平台, 角色转变, 影响因素, Covid-19, 突发传染病

Abstract: [Purpose/Significance] To explore the user role classification methods, key factors of role formation, transformation characteristics and differences of the Q&A platforms in the context of infectious disease outbreaks. [Method/Process] A total of 702,927 data related to Covid-19 epidemic were collected from Q&A platforms. The user roles were analyzed from the dimensions of participation and value. The influencing factors of community user role formation were constructed based on the information user factor, information factor and information environment factor. The key factors affecting the formation of different roles were analyzed by combining the multi-classification model and the SHapley Additive exPlanations (SHAP) model. The FP-growth association rule algorithm was used to mine behavior patterns and topic characteristics during the transformation of different roles. [Result/Conclusion] The results show that users tend to keep their roles unchanged, and the transformation direction is mainly towards active or diving roles. The amount of information is the key factor for the formation of different roles. There are significant differences in the extent of change in user role transformation characteristics in different transformation stages and user role transformation behaviors in all transformation stages.

Key words: user role, Zhihu Q&A platform, role transformation, influencing factors, Covid-19, infectious disease outbreak

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