Content-based user classifier to uncover information exchange in disaster-motivated networks

Disasters strike communities around the world, with a reduced time-frame for warning and action leaving behind high rates of damage, mortality, and years in rebuilding efforts. For the past decade, social media has indicated a positive role in communicating before, during, and after disasters. One i...

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Autores principales:	Pouria Babvey, Gabriela Gongora-Svartzman, Carlo Lipizzi, Jose E. Ramirez-Marquez
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/45cef6f85c3345bb970ca0717ad121bd
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spelling	oai:doaj.org-article:45cef6f85c3345bb970ca0717ad121bd2021-11-25T06:13:58ZContent-based user classifier to uncover information exchange in disaster-motivated networks1932-6203https://doaj.org/article/45cef6f85c3345bb970ca0717ad121bd2021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8594803/?tool=EBIhttps://doaj.org/toc/1932-6203Disasters strike communities around the world, with a reduced time-frame for warning and action leaving behind high rates of damage, mortality, and years in rebuilding efforts. For the past decade, social media has indicated a positive role in communicating before, during, and after disasters. One important question that remained un-investigated is that whether social media efficiently connect affected individuals to disaster relief agencies, and if not, how AI models can use historical data from previous disasters to facilitate information exchange between the two groups. In this study, the BERT model is first fine-tuned using historical data and then it is used to classify the tweets associated with hurricanes Dorian and Harvey based on the type of information provided; and alongside, the network between users is constructed based on the retweets and replies on Twitter. Afterwards, some network metrics are used to measure the diffusion rate of each type of disaster-motivated information. The results show that the messages by disaster eyewitnesses get the least spread while the posts by governments and media have the highest diffusion rates through the network. Additionally, the “cautions and advice” messages get the most spread among other information types while “infrastructure and utilities” and “affected individuals” messages get the least diffusion even compared with “sympathy and support”. The analysis suggests that facilitating the propagation of information provided by affected individuals, using AI models, will be a valuable strategy to pursue in order to accelerate communication between affected individuals and survival groups during the disaster and aftermath.Pouria BabveyGabriela Gongora-SvartzmanCarlo LipizziJose E. Ramirez-MarquezPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Pouria Babvey Gabriela Gongora-Svartzman Carlo Lipizzi Jose E. Ramirez-Marquez Content-based user classifier to uncover information exchange in disaster-motivated networks
description	Disasters strike communities around the world, with a reduced time-frame for warning and action leaving behind high rates of damage, mortality, and years in rebuilding efforts. For the past decade, social media has indicated a positive role in communicating before, during, and after disasters. One important question that remained un-investigated is that whether social media efficiently connect affected individuals to disaster relief agencies, and if not, how AI models can use historical data from previous disasters to facilitate information exchange between the two groups. In this study, the BERT model is first fine-tuned using historical data and then it is used to classify the tweets associated with hurricanes Dorian and Harvey based on the type of information provided; and alongside, the network between users is constructed based on the retweets and replies on Twitter. Afterwards, some network metrics are used to measure the diffusion rate of each type of disaster-motivated information. The results show that the messages by disaster eyewitnesses get the least spread while the posts by governments and media have the highest diffusion rates through the network. Additionally, the “cautions and advice” messages get the most spread among other information types while “infrastructure and utilities” and “affected individuals” messages get the least diffusion even compared with “sympathy and support”. The analysis suggests that facilitating the propagation of information provided by affected individuals, using AI models, will be a valuable strategy to pursue in order to accelerate communication between affected individuals and survival groups during the disaster and aftermath.
format	article
author	Pouria Babvey Gabriela Gongora-Svartzman Carlo Lipizzi Jose E. Ramirez-Marquez
author_facet	Pouria Babvey Gabriela Gongora-Svartzman Carlo Lipizzi Jose E. Ramirez-Marquez
author_sort	Pouria Babvey
title	Content-based user classifier to uncover information exchange in disaster-motivated networks
title_short	Content-based user classifier to uncover information exchange in disaster-motivated networks
title_full	Content-based user classifier to uncover information exchange in disaster-motivated networks
title_fullStr	Content-based user classifier to uncover information exchange in disaster-motivated networks
title_full_unstemmed	Content-based user classifier to uncover information exchange in disaster-motivated networks
title_sort	content-based user classifier to uncover information exchange in disaster-motivated networks
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/45cef6f85c3345bb970ca0717ad121bd
work_keys_str_mv	AT pouriababvey contentbaseduserclassifiertouncoverinformationexchangeindisastermotivatednetworks AT gabrielagongorasvartzman contentbaseduserclassifiertouncoverinformationexchangeindisastermotivatednetworks AT carlolipizzi contentbaseduserclassifiertouncoverinformationexchangeindisastermotivatednetworks AT joseeramirezmarquez contentbaseduserclassifiertouncoverinformationexchangeindisastermotivatednetworks
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Content-based user classifier to uncover information exchange in disaster-motivated networks

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