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Identifying and Ranking Common COVID-19 Symptoms From Tweets in Arabic: Content Analysis

BACKGROUND: A substantial amount of COVID-19–related data is generated by Twitter users every day. Self-reports of COVID-19 symptoms on Twitter can reveal a great deal about the disease and its prevalence in the community. In particular, self-reports can be used as a valuable resource to learn more...

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Detalles Bibliográficos
Autores principales: Alanazi, Eisa, Alashaikh, Abdulaziz, Alqurashi, Sarah, Alanazi, Aued
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7677592/
https://www.ncbi.nlm.nih.gov/pubmed/33119539
http://dx.doi.org/10.2196/21329
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author Alanazi, Eisa
Alashaikh, Abdulaziz
Alqurashi, Sarah
Alanazi, Aued
author_facet Alanazi, Eisa
Alashaikh, Abdulaziz
Alqurashi, Sarah
Alanazi, Aued
author_sort Alanazi, Eisa
collection PubMed
description BACKGROUND: A substantial amount of COVID-19–related data is generated by Twitter users every day. Self-reports of COVID-19 symptoms on Twitter can reveal a great deal about the disease and its prevalence in the community. In particular, self-reports can be used as a valuable resource to learn more about common symptoms and whether their order of appearance differs among different groups in the community. These data may be used to develop a COVID-19 risk assessment system that is tailored toward a specific group of people. OBJECTIVE: The aim of this study was to identify the most common symptoms reported by patients with COVID-19, as well as the order of symptom appearance, by examining tweets in Arabic. METHODS: We searched Twitter posts in Arabic for personal reports of COVID-19 symptoms from March 1 to May 27, 2020. We identified 463 Arabic users who had tweeted about testing positive for COVID-19 and extracted the symptoms they associated with the disease. Furthermore, we asked them directly via personal messaging to rank the appearance of the first 3 symptoms they had experienced immediately before (or after) their COVID-19 diagnosis. Finally, we tracked their Twitter timeline to identify additional symptoms that were mentioned within ±5 days from the day of the first tweet on their COVID-19 diagnosis. In total, 270 COVID-19 self-reports were collected, and symptoms were (at least partially) ranked. RESULTS: The collected self-reports contained 893 symptoms from 201 (74%) male and 69 (26%) female Twitter users. The majority (n=270, 82%) of the tracked users were living in Saudi Arabia (n=125, 46%) and Kuwait (n=98, 36%). Furthermore, 13% (n=36) of the collected reports were from asymptomatic individuals. Of the 234 users with symptoms, 66% (n=180) provided a chronological order of appearance for at least 3 symptoms. Fever (n=139, 59%), headache (n=101, 43%), and anosmia (n=91, 39%) were the top 3 symptoms mentioned in the self-reports. Additionally, 28% (n=65) reported that their COVID-19 experience started with a fever, 15% (n=34) with a headache, and 12% (n=28) with anosmia. Of the 110 symptomatic cases from Saudi Arabia, the most common 3 symptoms were fever (n=65, 59%), anosmia (n=46, 42%), and headache (n=42, 38%). CONCLUSIONS: This study identified the most common symptoms of COVID-19 from tweets in Arabic. These symptoms can be further analyzed in clinical settings and may be incorporated into a real-time COVID-19 risk estimator.
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spelling pubmed-76775922020-11-23 Identifying and Ranking Common COVID-19 Symptoms From Tweets in Arabic: Content Analysis Alanazi, Eisa Alashaikh, Abdulaziz Alqurashi, Sarah Alanazi, Aued J Med Internet Res Original Paper BACKGROUND: A substantial amount of COVID-19–related data is generated by Twitter users every day. Self-reports of COVID-19 symptoms on Twitter can reveal a great deal about the disease and its prevalence in the community. In particular, self-reports can be used as a valuable resource to learn more about common symptoms and whether their order of appearance differs among different groups in the community. These data may be used to develop a COVID-19 risk assessment system that is tailored toward a specific group of people. OBJECTIVE: The aim of this study was to identify the most common symptoms reported by patients with COVID-19, as well as the order of symptom appearance, by examining tweets in Arabic. METHODS: We searched Twitter posts in Arabic for personal reports of COVID-19 symptoms from March 1 to May 27, 2020. We identified 463 Arabic users who had tweeted about testing positive for COVID-19 and extracted the symptoms they associated with the disease. Furthermore, we asked them directly via personal messaging to rank the appearance of the first 3 symptoms they had experienced immediately before (or after) their COVID-19 diagnosis. Finally, we tracked their Twitter timeline to identify additional symptoms that were mentioned within ±5 days from the day of the first tweet on their COVID-19 diagnosis. In total, 270 COVID-19 self-reports were collected, and symptoms were (at least partially) ranked. RESULTS: The collected self-reports contained 893 symptoms from 201 (74%) male and 69 (26%) female Twitter users. The majority (n=270, 82%) of the tracked users were living in Saudi Arabia (n=125, 46%) and Kuwait (n=98, 36%). Furthermore, 13% (n=36) of the collected reports were from asymptomatic individuals. Of the 234 users with symptoms, 66% (n=180) provided a chronological order of appearance for at least 3 symptoms. Fever (n=139, 59%), headache (n=101, 43%), and anosmia (n=91, 39%) were the top 3 symptoms mentioned in the self-reports. Additionally, 28% (n=65) reported that their COVID-19 experience started with a fever, 15% (n=34) with a headache, and 12% (n=28) with anosmia. Of the 110 symptomatic cases from Saudi Arabia, the most common 3 symptoms were fever (n=65, 59%), anosmia (n=46, 42%), and headache (n=42, 38%). CONCLUSIONS: This study identified the most common symptoms of COVID-19 from tweets in Arabic. These symptoms can be further analyzed in clinical settings and may be incorporated into a real-time COVID-19 risk estimator. JMIR Publications 2020-11-18 /pmc/articles/PMC7677592/ /pubmed/33119539 http://dx.doi.org/10.2196/21329 Text en ©Eisa Alanazi, Abdulaziz Alashaikh, Sarah Alqurashi, Aued Alanazi. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 18.11.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Alanazi, Eisa
Alashaikh, Abdulaziz
Alqurashi, Sarah
Alanazi, Aued
Identifying and Ranking Common COVID-19 Symptoms From Tweets in Arabic: Content Analysis
title Identifying and Ranking Common COVID-19 Symptoms From Tweets in Arabic: Content Analysis
title_full Identifying and Ranking Common COVID-19 Symptoms From Tweets in Arabic: Content Analysis
title_fullStr Identifying and Ranking Common COVID-19 Symptoms From Tweets in Arabic: Content Analysis
title_full_unstemmed Identifying and Ranking Common COVID-19 Symptoms From Tweets in Arabic: Content Analysis
title_short Identifying and Ranking Common COVID-19 Symptoms From Tweets in Arabic: Content Analysis
title_sort identifying and ranking common covid-19 symptoms from tweets in arabic: content analysis
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7677592/
https://www.ncbi.nlm.nih.gov/pubmed/33119539
http://dx.doi.org/10.2196/21329
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