微博中基于用户特征的突发事件信息传播分析

李思佳, 郑德铭, 孙正义

农业图书情报学报. 2023, 35(11): 86-97

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农业图书情报学报 ›› 2023, Vol. 35 ›› Issue (11) : 86-97. DOI: 10.13998/j.cnki.issn1002-1248.23-0812
研究论文

微博中基于用户特征的突发事件信息传播分析

  • 李思佳1, 郑德铭1, 孙正义2
作者信息 +

Analysis of Information Dissemination of Emergencies Based on Weibo User Characteristics

  • LI Sijia1, ZHENG Deming1, SUN Zhengyi2
Author information +
History +

摘要

[目的/意义]随着社交媒体的普及和发展,微博已成为重要的信息传播平台。理解微博中基于用户特征的突发事件信息传播规律,对于把握事件发展态势、评估信息影响力以及制定有效的应对策略具有重要意义。[方法/过程]通过选取4类突发事件(自然灾害、事故灾难、公共卫生事件、社会安全事件)的典型案例作为研究对象,爬取微博平台上的信息传播用户数据,分析4类突发事件信息传播用户特征及传播规律的差异与共性。[结果/结论]4类突发事件信息传播的受众都具有明显的地域性,经济发达的省份用户关注度普遍较高;各类突发事件的传播用户群普遍集中在传播力强的活跃用户,以及传播力和影响力均较弱的草根用户上,伴随有少量的影响力较强的权威用户参与传播;不同类型的突发事件,传播受众广泛性和性别分布均有所差异。

Abstract

[Purpose/Significance] With the popularity and development of social media, Weibo has become an important information transmission platform. Understanding the law of emergency information transmission based on the characteristics of Weibo users is of great significance for grasping the development trend of events, evaluating the influence of information, and formulating effective response strategies. [Methods/Process] Selecting typical cases of four types of emergencies (natural disasters, accidents, public health events, and social security events) as the research objects, this paper crawled the user data of the information transmission on the Weibo platform and analyzed the distributions of user gender, user geographical distribution, user attention and fan number and user type of the information transmission for these events, in order to reveal the differences and similarities of the user characteristics and transmission laws for the four types of emergencies. [Results/Conclusions] As for the similarities of the user characteristics and transmission laws, first, the audiences of the four types of emergencies have obvious regional characteristics, and the users in economically developed provinces generally have a higher attention rate. Second, the transmission users of all types of emergencies are generally concentrated on active users and grassroot users with strong and weak propagation power respectively, accompanied by a small number of authoritative users with strong influence. However, there are differences in the broadness and gender distribution of the audiences for different types of emergencies. First, natural disasters have a wider spread of the audiences and relatively higher attentiveness; accidents and public health events tend to be local events with relatively low attentiveness, whose audiences may be more concentrated in and around the affected areas; the audience spread of social security events is scattered depending on the nature of the event, the scope of influence as well as the communication channels. Second, female users pay more attention to natural disasters, public health events and social security events, while male users pay more attention to accidents, which can be attributed to the different emotional orientations and psychological characteristics associated with users of different genders. These results provide insights for the formulation of targeted guidance strategies for information dissemination. In future studies, we will collect data on other social media platforms, obtain more information on user characteristics through different channels, and introduce more in-depth analysis methods and indicators to comprehensively reveal the dynamic mechanisms of emergency information dissemination, thus improving the accuracy and effectiveness of the research.

关键词

社交媒体 / 微博 / 信息传播 / 用户特征 / 突发事件

Key words

social media / Weibo / information dissemination / user characteristics / emergency

引用本文

导出引用
李思佳, 郑德铭, 孙正义. 微博中基于用户特征的突发事件信息传播分析. 农业图书情报学报. 2023, 35(11): 86-97 https://doi.org/10.13998/j.cnki.issn1002-1248.23-0812
LI Sijia, ZHENG Deming, SUN Zhengyi. Analysis of Information Dissemination of Emergencies Based on Weibo User Characteristics. Journal of Library and Information Sciences in Agriculture. 2023, 35(11): 86-97 https://doi.org/10.13998/j.cnki.issn1002-1248.23-0812

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