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Public Perceptions around mHealth Applications during COVID-19 Pandemic: A Network and Sentiment Analysis of Tweets in Saudi Arabia

A series of mitigation efforts were implemented in response to the COVID-19 pandemic in Saudi Arabia, including the development of mobile health applications (mHealth apps) for the public. Assessing the acceptability of mHealth apps among the public is crucial. This study aimed to use Twitter to und...

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Autores principales: Binkheder, Samar, Aldekhyyel, Raniah N., AlMogbel, Alanoud, Al-Twairesh, Nora, Alhumaid, Nuha, Aldekhyyel, Shahad N., Jamal, Amr A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8708161/
https://www.ncbi.nlm.nih.gov/pubmed/34948997
http://dx.doi.org/10.3390/ijerph182413388
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author Binkheder, Samar
Aldekhyyel, Raniah N.
AlMogbel, Alanoud
Al-Twairesh, Nora
Alhumaid, Nuha
Aldekhyyel, Shahad N.
Jamal, Amr A.
author_facet Binkheder, Samar
Aldekhyyel, Raniah N.
AlMogbel, Alanoud
Al-Twairesh, Nora
Alhumaid, Nuha
Aldekhyyel, Shahad N.
Jamal, Amr A.
author_sort Binkheder, Samar
collection PubMed
description A series of mitigation efforts were implemented in response to the COVID-19 pandemic in Saudi Arabia, including the development of mobile health applications (mHealth apps) for the public. Assessing the acceptability of mHealth apps among the public is crucial. This study aimed to use Twitter to understand public perceptions around the use of six Saudi mHealth apps used during COVID-19: “Sehha”, “Mawid”, “Sehhaty”, “Tetamman”, “Tawakkalna”, and “Tabaud”. We used two methodological approaches: network and sentiment analysis. We retrieved Twitter data using specific mHealth apps-related keywords. After including relevant tweets, our final mHealth app networks consisted of a total of 4995 Twitter users and 8666 conversational relationships. The largest networks in size (i.e., the number of users) and volume (i.e., the conversational relationships) among all were “Tawakkalna” followed by “Tabaud”, and their conversations were led by diverse governmental accounts. In contrast, the four remaining mHealth networks were mainly led by the health sector and media. Our sentiment analysis approach included five classes and showed that most conversations were neutral, which included facts or information pieces and general inquires. For the automated sentiment classifier, we used Support Vector Machine with AraVec embeddings as it outperformed the other tested classifiers. The sentiment classifier showed an accuracy, precision, recall, and F1-score of 85%. Future studies can use social media and real-time analytics to improve mHealth apps’ services and user experience, especially during health crises.
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spelling pubmed-87081612021-12-25 Public Perceptions around mHealth Applications during COVID-19 Pandemic: A Network and Sentiment Analysis of Tweets in Saudi Arabia Binkheder, Samar Aldekhyyel, Raniah N. AlMogbel, Alanoud Al-Twairesh, Nora Alhumaid, Nuha Aldekhyyel, Shahad N. Jamal, Amr A. Int J Environ Res Public Health Article A series of mitigation efforts were implemented in response to the COVID-19 pandemic in Saudi Arabia, including the development of mobile health applications (mHealth apps) for the public. Assessing the acceptability of mHealth apps among the public is crucial. This study aimed to use Twitter to understand public perceptions around the use of six Saudi mHealth apps used during COVID-19: “Sehha”, “Mawid”, “Sehhaty”, “Tetamman”, “Tawakkalna”, and “Tabaud”. We used two methodological approaches: network and sentiment analysis. We retrieved Twitter data using specific mHealth apps-related keywords. After including relevant tweets, our final mHealth app networks consisted of a total of 4995 Twitter users and 8666 conversational relationships. The largest networks in size (i.e., the number of users) and volume (i.e., the conversational relationships) among all were “Tawakkalna” followed by “Tabaud”, and their conversations were led by diverse governmental accounts. In contrast, the four remaining mHealth networks were mainly led by the health sector and media. Our sentiment analysis approach included five classes and showed that most conversations were neutral, which included facts or information pieces and general inquires. For the automated sentiment classifier, we used Support Vector Machine with AraVec embeddings as it outperformed the other tested classifiers. The sentiment classifier showed an accuracy, precision, recall, and F1-score of 85%. Future studies can use social media and real-time analytics to improve mHealth apps’ services and user experience, especially during health crises. MDPI 2021-12-20 /pmc/articles/PMC8708161/ /pubmed/34948997 http://dx.doi.org/10.3390/ijerph182413388 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Binkheder, Samar
Aldekhyyel, Raniah N.
AlMogbel, Alanoud
Al-Twairesh, Nora
Alhumaid, Nuha
Aldekhyyel, Shahad N.
Jamal, Amr A.
Public Perceptions around mHealth Applications during COVID-19 Pandemic: A Network and Sentiment Analysis of Tweets in Saudi Arabia
title Public Perceptions around mHealth Applications during COVID-19 Pandemic: A Network and Sentiment Analysis of Tweets in Saudi Arabia
title_full Public Perceptions around mHealth Applications during COVID-19 Pandemic: A Network and Sentiment Analysis of Tweets in Saudi Arabia
title_fullStr Public Perceptions around mHealth Applications during COVID-19 Pandemic: A Network and Sentiment Analysis of Tweets in Saudi Arabia
title_full_unstemmed Public Perceptions around mHealth Applications during COVID-19 Pandemic: A Network and Sentiment Analysis of Tweets in Saudi Arabia
title_short Public Perceptions around mHealth Applications during COVID-19 Pandemic: A Network and Sentiment Analysis of Tweets in Saudi Arabia
title_sort public perceptions around mhealth applications during covid-19 pandemic: a network and sentiment analysis of tweets in saudi arabia
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8708161/
https://www.ncbi.nlm.nih.gov/pubmed/34948997
http://dx.doi.org/10.3390/ijerph182413388
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