Spatiotemporal Evolution of the Online Social Network after a Natural Disaster

Social media has been a vital channel for communicating and broadcasting disaster-related information. However, the global spatiotemporal patterns of social media users’ activities, interactions, and connections after a natural disaster remain unclear. Hence, we integrated geocoding, geovisualizatio...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Shi Shen, Junwang Huang, Changxiu Cheng, Ting Zhang, Nikita Murzintcev, Peichao Gao
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	spatiotemporal pattern social media social network natural disaster scaling law Geography (General) G1-922
Acceso en línea:	https://doaj.org/article/a7394fd56247475a8acacdd6d6bf38ff
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:a7394fd56247475a8acacdd6d6bf38ff
record_format	dspace
spelling	oai:doaj.org-article:a7394fd56247475a8acacdd6d6bf38ff2021-11-25T17:52:54ZSpatiotemporal Evolution of the Online Social Network after a Natural Disaster10.3390/ijgi101107442220-9964https://doaj.org/article/a7394fd56247475a8acacdd6d6bf38ff2021-11-01T00:00:00Zhttps://www.mdpi.com/2220-9964/10/11/744https://doaj.org/toc/2220-9964Social media has been a vital channel for communicating and broadcasting disaster-related information. However, the global spatiotemporal patterns of social media users’ activities, interactions, and connections after a natural disaster remain unclear. Hence, we integrated geocoding, geovisualization, and complex network methods to illustrate and analyze the online social network’s spatiotemporal evolution. Taking the super typhoon Haiyan as a case, we constructed a retweeting network and mapped this network according to the tweets’ location information. The results show that (1) the distribution of in-degree and out-degree follow power-law and retweeting networks are scale-free. (2) A local catastrophe could attract significant global interest but with strong geographical heterogeneity. The super typhoon Haiyan especially attracted attention from the United States, Europe, and Australia, in which users are more active in posting and forwarding disaster-related tweets than other regions (except the Philippines). (3) The users’ interactions and connections are also significantly different between countries and regions. Connections and interactions between the Philippines and the United States, Europe, and Australia were much closer than in other regions. Therefore, the agencies and platforms should also pay attention to other countries and regions outside the disaster area to provide more valuable information for the local people.Shi ShenJunwang HuangChangxiu ChengTing ZhangNikita MurzintcevPeichao GaoMDPI AGarticlespatiotemporal patternsocial mediasocial networknatural disasterscaling lawGeography (General)G1-922ENISPRS International Journal of Geo-Information, Vol 10, Iss 744, p 744 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	spatiotemporal pattern social media social network natural disaster scaling law Geography (General) G1-922
spellingShingle	spatiotemporal pattern social media social network natural disaster scaling law Geography (General) G1-922 Shi Shen Junwang Huang Changxiu Cheng Ting Zhang Nikita Murzintcev Peichao Gao Spatiotemporal Evolution of the Online Social Network after a Natural Disaster
description	Social media has been a vital channel for communicating and broadcasting disaster-related information. However, the global spatiotemporal patterns of social media users’ activities, interactions, and connections after a natural disaster remain unclear. Hence, we integrated geocoding, geovisualization, and complex network methods to illustrate and analyze the online social network’s spatiotemporal evolution. Taking the super typhoon Haiyan as a case, we constructed a retweeting network and mapped this network according to the tweets’ location information. The results show that (1) the distribution of in-degree and out-degree follow power-law and retweeting networks are scale-free. (2) A local catastrophe could attract significant global interest but with strong geographical heterogeneity. The super typhoon Haiyan especially attracted attention from the United States, Europe, and Australia, in which users are more active in posting and forwarding disaster-related tweets than other regions (except the Philippines). (3) The users’ interactions and connections are also significantly different between countries and regions. Connections and interactions between the Philippines and the United States, Europe, and Australia were much closer than in other regions. Therefore, the agencies and platforms should also pay attention to other countries and regions outside the disaster area to provide more valuable information for the local people.
format	article
author	Shi Shen Junwang Huang Changxiu Cheng Ting Zhang Nikita Murzintcev Peichao Gao
author_facet	Shi Shen Junwang Huang Changxiu Cheng Ting Zhang Nikita Murzintcev Peichao Gao
author_sort	Shi Shen
title	Spatiotemporal Evolution of the Online Social Network after a Natural Disaster
title_short	Spatiotemporal Evolution of the Online Social Network after a Natural Disaster
title_full	Spatiotemporal Evolution of the Online Social Network after a Natural Disaster
title_fullStr	Spatiotemporal Evolution of the Online Social Network after a Natural Disaster
title_full_unstemmed	Spatiotemporal Evolution of the Online Social Network after a Natural Disaster
title_sort	spatiotemporal evolution of the online social network after a natural disaster
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a7394fd56247475a8acacdd6d6bf38ff
work_keys_str_mv	AT shishen spatiotemporalevolutionoftheonlinesocialnetworkafteranaturaldisaster AT junwanghuang spatiotemporalevolutionoftheonlinesocialnetworkafteranaturaldisaster AT changxiucheng spatiotemporalevolutionoftheonlinesocialnetworkafteranaturaldisaster AT tingzhang spatiotemporalevolutionoftheonlinesocialnetworkafteranaturaldisaster AT nikitamurzintcev spatiotemporalevolutionoftheonlinesocialnetworkafteranaturaldisaster AT peichaogao spatiotemporalevolutionoftheonlinesocialnetworkafteranaturaldisaster
_version_	1718411878749700096

Spatiotemporal Evolution of the Online Social Network after a Natural Disaster

Ejemplares similares