Dynamic evolution of information diffusion networks of news agencies in emergencies: a case study of microblogs of urban fire disasters on Sina Weibo
Dynamic evolution of information diffusion networks of news agencies in emergencies: a case study of microblogs of urban fire disasters on Sina Weibo
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DOI：
10.1007/s11042-023-16498-0
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发表时间：
2023-08-19
影响因子：
3.6
通讯作者：
He，Hanyue
中科院分区：
文献类型：
--
作者：
Chen，Xuelong;Chen，Yiping;He，Hanyue
对于信息传播至关重要的是，有影响力的新闻机构可以在社交媒体平台上发布突发事件，不仅有助于引导舆论，增强公众对突发事件的态势感知和反应能力，还可以减少突发事件期间的社会恐慌，谣言传播和负面舆论。为研究突发事件信息的传播模式，收集了国内最具影响力的两家新闻机构发布的35条微博原创微博上关于11起城市火灾事件的发布和转发信息，并对其进行分析，探讨突发事件信息的动态扩散规律和峰值扩散过程中的拓扑结构演变。结果表明，通讯社信息扩散网络的拓扑结构包括超级巨星结构、N星结构、星系结构、环状结构和综合结构，其特征在于网络的拓扑结构和转发率等数值特征。在IDN的动态演化过程中，综合结构具有最佳的信息扩散和控制能力，从而优化了社交媒体平台上应急信息的及时有效管理。该结构中最主要的枢纽节点的出度达到2000，是其它结构的近5-10倍。通过对网络拓扑演化和统计特性的分析，填补了动力学拓扑演化和统计特性演化结合的差距。这项研究的结果可以适用于类似的灾难和社交媒体平台。
Critical to information diffusion, influential news agencies can publicize emergencies on social media platforms, not only helping to guide public opinion and enhance the public’s situational awareness and responsiveness to emergencies, but also reducing social panic, rumor propagation, and negative public opinions during emergencies. To study diffusion patterns of emergency information, postings and repostings about 11 urban fire disasters on 35 original microblogs posted by the two most influential news agencies were collected and analyzed by exploring the law of dynamic information diffusion and the evolution of the topology structure during peak diffusion. Results showed that the topological structures of the information diffusion networks (IDN) of news agencies comprise superstar, N-star, galaxy, ring, and comprehensive structures, characterized by topological structure and numerical characteristics such as reposting rate. In the dynamic evolution of IDN, the comprehensive structure has the best information diffusion and control, and thus serves to optimize the timely and effective management of emergency information on social media platforms. The out-degrees of the most dominant hub node in this structure achieved 2000, which is nearly 5–10 times that of the other structures. This work fills the gap in the combination of dynamical topological and statistical characteristics evolution by analyzing the topological evolution and statistical characteristics of networks. The findings of this study can be applicable to similar disasters and social media platforms.