Exploring the Impact of COVID-19 on Social Life by Deep Learning

Due to the globalisation of the COVID-19 pandemic, and the expansion of social media as the main source of information for many people, there have been a great variety of different reactions surrounding the topic. The World Health Organization (WHO) announced in December 2020 that they were currentl...

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Autores principales:	Jose Antonio Jijon-Vorbeck, Isabel Segura-Bedmar
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	natural language processing sentiment analysis multi-classification machine learning deep learning COVID-19 Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/5d98e00a1ca6423880038f03da244a14
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spelling	oai:doaj.org-article:5d98e00a1ca6423880038f03da244a142021-11-25T17:58:32ZExploring the Impact of COVID-19 on Social Life by Deep Learning10.3390/info121104592078-2489https://doaj.org/article/5d98e00a1ca6423880038f03da244a142021-11-01T00:00:00Zhttps://www.mdpi.com/2078-2489/12/11/459https://doaj.org/toc/2078-2489Due to the globalisation of the COVID-19 pandemic, and the expansion of social media as the main source of information for many people, there have been a great variety of different reactions surrounding the topic. The World Health Organization (WHO) announced in December 2020 that they were currently fighting an “infodemic” in the same way as they were fighting the pandemic. An “infodemic” relates to the spread of information that is not controlled or filtered, and can have a negative impact on society. If not managed properly, an aggressive or negative tweet can be very harmful and misleading among its recipients. Therefore, authorities at WHO have called for action and asked the academic and scientific community to develop tools for managing the infodemic by the use of digital technologies and data science. The goal of this study is to develop and apply natural language processing models using deep learning to classify a collection of tweets that refer to the COVID-19 pandemic. Several simpler and widely used models are applied first and serve as a benchmark for deep learning methods, such as Long Short-Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers (BERT). The results of the experiments show that the deep learning models outperform the traditional machine learning algorithms. The best approach is the BERT-based model.Jose Antonio Jijon-VorbeckIsabel Segura-BedmarMDPI AGarticlenatural language processingsentiment analysismulti-classificationmachine learningdeep learningCOVID-19Information technologyT58.5-58.64ENInformation, Vol 12, Iss 459, p 459 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	natural language processing sentiment analysis multi-classification machine learning deep learning COVID-19 Information technology T58.5-58.64
spellingShingle	natural language processing sentiment analysis multi-classification machine learning deep learning COVID-19 Information technology T58.5-58.64 Jose Antonio Jijon-Vorbeck Isabel Segura-Bedmar Exploring the Impact of COVID-19 on Social Life by Deep Learning
description	Due to the globalisation of the COVID-19 pandemic, and the expansion of social media as the main source of information for many people, there have been a great variety of different reactions surrounding the topic. The World Health Organization (WHO) announced in December 2020 that they were currently fighting an “infodemic” in the same way as they were fighting the pandemic. An “infodemic” relates to the spread of information that is not controlled or filtered, and can have a negative impact on society. If not managed properly, an aggressive or negative tweet can be very harmful and misleading among its recipients. Therefore, authorities at WHO have called for action and asked the academic and scientific community to develop tools for managing the infodemic by the use of digital technologies and data science. The goal of this study is to develop and apply natural language processing models using deep learning to classify a collection of tweets that refer to the COVID-19 pandemic. Several simpler and widely used models are applied first and serve as a benchmark for deep learning methods, such as Long Short-Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers (BERT). The results of the experiments show that the deep learning models outperform the traditional machine learning algorithms. The best approach is the BERT-based model.
format	article
author	Jose Antonio Jijon-Vorbeck Isabel Segura-Bedmar
author_facet	Jose Antonio Jijon-Vorbeck Isabel Segura-Bedmar
author_sort	Jose Antonio Jijon-Vorbeck
title	Exploring the Impact of COVID-19 on Social Life by Deep Learning
title_short	Exploring the Impact of COVID-19 on Social Life by Deep Learning
title_full	Exploring the Impact of COVID-19 on Social Life by Deep Learning
title_fullStr	Exploring the Impact of COVID-19 on Social Life by Deep Learning
title_full_unstemmed	Exploring the Impact of COVID-19 on Social Life by Deep Learning
title_sort	exploring the impact of covid-19 on social life by deep learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5d98e00a1ca6423880038f03da244a14
work_keys_str_mv	AT joseantoniojijonvorbeck exploringtheimpactofcovid19onsociallifebydeeplearning AT isabelsegurabedmar exploringtheimpactofcovid19onsociallifebydeeplearning
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Exploring the Impact of COVID-19 on Social Life by Deep Learning

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