Analysing Information Diffusion in Natural Hazards using Retweets - a Case Study of 2018 Winter Storm Diego
Analysing Information Diffusion in Natural Hazards using Retweets - a Case Study of 2018 Winter Storm Diego
复制标题
使用转发分析自然灾害中的信息扩散 - 以 2018 年冬季风暴迭戈为例
DOI：
10.1080/19475683.2021.1954086
复制
发表时间：
2021
期刊：
影响因子：
5
通讯作者：
Qiang, Yi
中科院分区：
文献类型：
--
作者：
Xu, Jinwen;Qiang, Yi
灾害期间社交媒体上的信息传播是衡量社区复原力的重要指标。冬季风暴在美国是一种常见的自然灾害，经常给人类社会造成不利的社会经济影响。了解人们在冬季风暴期间的感知和行为对于减轻负面影响和促进社区复原力非常重要。这项研究对2018年12月冬季风暴迭戈期间的Twitter数据应用了文本挖掘和空间分析方法。与以往关注原创推文的研究不同，本研究利用转发来模拟美国毗邻地区的信息扩散，并分析了不同主题中信息流的地理分布。比较了风暴相关转发在不同话题中的扩散程度和扩散方向。核密度图和标准差椭圆被用来模拟不同话题的转发量的空间分布。结果显示，受灾地区以外的人比受灾地区的人对风暴表达了更多的负面情绪。此外，还发现了转发密度的距离衰减，并且不同主题的衰减率不同。这些分析结果将为通过社交媒体平台进行救灾、信息交流和广播提供支持。
Information diffusion on social media during disasters is an important indicator of community resilience. As a common natural hazard in the U.S., winter storms often cause adverse socio-economic impacts on human society. Understanding people’s perception and behaviours during winter storms is important to mitigate negative impacts and promote community resilience. This study applies text mining and spatial analysis methods on Twitter data during Winter Storm Diego on 2018 December. Different from previous studies focusing on original tweets, this study utilized retweets to model information diffusion in the contiguous United States and analysed the geographic distribution of information flows in various topics. The diffusion extent and direction of the storm-related retweets were compared among different topics. Kernel density maps and standard deviational ellipse were applied to model the spatial distribution of the retweets in different topics. The result shows that people outside of the affected areas expressed more negative sentiment towards the storm than people in the affected areas. Also, distance decay of retweet density has been found and the decay rate differs in different topics. These findings of the analyses will provide support for disaster relief, information communication and broadcasting through social media platforms.
影响因子：
9.9
作者：
Lachlan, Kenneth A.;Spence, Patric R.;Del Greco, Maria
通讯作者：
Del Greco, Maria
社交媒体参与设计：老年护理援助的见解
影响因子：
7
作者：
P. Spagnoletti;A. Resca;Øystein Sæbø
通讯作者：
Øystein Sæbø
DOI：
10.1080/15230406.2018.1434834
发表时间：
2019-05-04
影响因子：
2.5
作者：
Jiang, Yuqin;Li, Zhenlong;Ye, Xinyue
通讯作者：
Ye, Xinyue