图书情报工作 ›› 2021, Vol. 65 ›› Issue (19): 16-25.DOI: 10.13266/j.issn.0252-3116.2021.19.002

所属专题: 突发公共卫生事件中网络谣言治理及个人信息保护研究

• 专题:突发公共卫生事件中网络谣言治理及个人信息保护研究 • 上一篇    下一篇

社交媒体中突发公共卫生事件网络辟谣信息主体研究

贾若男1, 王晰巍1,2,3, 孙玉姣1   

  1. 1 吉林大学管理学院 长春 130022;
    2 吉林大学大数据管理研究中心 长春 130022;
    3 吉林大学国家发展与安全研究院网络空间治理研究中心 长春 130022
  • 收稿日期:2021-04-12 修回日期:2021-08-05 出版日期:2021-10-05 发布日期:2021-10-09
  • 通讯作者: 王晰巍(ORCID:0000-0002-5850-0126),主任,教授,博士生导师,通讯作者,E-mail:wxw_mail@163.com
  • 作者简介:贾若男(ORCID:0000-0002-4262-7982),博士研究生;孙玉姣(ORCID:0000-0001-7265-8969),本科生。
  • 基金资助:
    本文系国家社会科学基金重大项目"大数据驱动的社交网络舆情主题图谱构建及调控策略研究"(项目编号:18ZDA310)研究成果之一。

Research on the Subject of Information to Refute Rumors of Public Health Emergencies in Social Media

Jia Ruonan1, Wang Xiwei1,2,3, Sun Yujiao1   

  1. 1 School of Management, Jilin University, Changchun 130022;
    2 Research Center for Big Data Management, Jilin University, Changchun 130022;
    3 Cyberspace Governance Research Center, National Academy of Development and Security, Jilin University, Changchun 130022
  • Received:2021-04-12 Revised:2021-08-05 Online:2021-10-05 Published:2021-10-09

摘要: [目的/意义] 从多角度分析网络辟谣信息主体的类型、相互关系、社区结构和辟谣信息传播效果,有助于发现关键的辟谣信息主体和有效的网络辟谣信息扩散模式,对于加强突发公共卫生事件期间的舆情引导及维护社会稳定具有重要作用。[方法/过程] 选取新冠肺炎疫情期间"双黄连"辟谣事件,通过Neo4j构建辟谣主体关系网络,利用Louvain社区发现算法划分网络社区;通过内容分析和回归分析对网络辟谣信息主体的内容特征和辟谣策略进行比较和分析,并构建"主体-内容"二模网络,以分析不同信息主体和网络社区如何推动社交媒体中的辟谣信息传播以及行之有效的辟谣方式和辟谣策略。[结果/结论] 研究结果发现,政府和大众媒体是网络辟谣中的主要行为者。政府最普遍使用反驳谣言的策略,大众媒体则与之相反。内容特征对辟谣信息传播效果具有不同的影响。

关键词: 社交媒体, 突发公共卫生事件, 网络辟谣, 信息主体

Abstract: [Purpose/significance] Analyzing the types, mutual relationships, community structure, and dissemination effects of the information on the Internet from multiple angles will help to discover the key information subjects and the effective spread of the information on the Internet. It plays an important role in strengthening the guidance of public opinion during public health emergencies and maintaining social stability.[Method/process] The article selected the "Shuanghuanglian" rumors during the new crown pneumonia epidemic, and built a network of rumor-defying subjects through Neo4j, then detected the network community using Louvain algorithm. Through content analysis and regression analysis, this paper analyzed the characteristics of the information content and the strategies of the rumor-defying subjects, and constructed the subject-content two-mode network, to explore how different information subjects and communities promote the dissemination of rumor-refuting information in social media, as well as effective methods and strategies for rumor-refuting.[Result/conclusion] The results of the study found that the government and mass media were the main actors in online rumor-refuting. The government most used the strategy of countering rumors, while the mass media did the opposite. Content characteristics have different effects on the effectiveness of rumor-refuting information dissemination.

Key words: social media, public health emergency, rumor-refuting, subject of information

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