A comparative analysis for spatio-temporal spreading patterns of emergency news

被引：12
|
作者：
Si, Mingjiao [1 ,2 ]
Cui, Lizhen [1 ,2 ]
Guo, Wei [1 ,2 ]
Li, Qingzhong [1 ,2 ]
Liu, Lei [1 ,2 ]
Lu, Xudong [1 ,2 ]
Lu, Xin [3 ]
机构：
[1] Shandong Univ, Sch Software, Jinan 250101, Peoples R China
[2] Joint SDU NTU Ctr Artificial Intelligence Res C F, Jinan 250101, Peoples R China
[3] Natl Univ Def Technol, Coll Syst Engn, Changsha 410073, Peoples R China
来源：
SCIENTIFIC REPORTS | 2020年 / 10卷 / 01期
关键词：
INFORMATION DIFFUSION; SOCIAL MEDIA; NETWORKS; TWITTER; TWEETS;
D O I：
10.1038/s41598-020-76162-7
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
Understanding the propagation characteristics of online emergency news communication is of great importance to guiding emergency management and supporting the dissemination of vital information. However, existing methods are limited to the analysis of the dissemination of online information pertaining to a specific disaster event. To study the quantification of the general spreading patterns and unique dynamic evolution of emergency-related information, we build a systematic, comprehensive evaluation framework and apply it to 81 million reposts from Sina Weibo, Chinese largest online microblogging platform, and perform a comparative analysis with four other types of online information (political, social, techs, and entertainment news). We find that the spreading of emergency news generally exhibits a shorter life cycle, a shorter active period, and fewer fluctuations in the aftermath of the peak than other types of news, while propagation is limited to a few steps from the source. Furthermore, compared with other types of news, fewer users tend to repost the same piece of news multiple times, while user influence (which depends on the number of fans) has the least impact on the number of reposts for news of emergencies. These comparative results provide insights that will be useful in the context of disaster relief, emergency management, and other communication path prediction applications.
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收藏
页数：13
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