This paper is in the following e-collection/theme issue:

JMIR Theme Issue: COVID-19 Special Issue (2452) Infodemiology and Infoveillance (1349) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) Outbreak and Pandemic Preparedness and Management (1727) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517)

Published on in Vol 22, No 11 (2020): November

Preprints (earlier versions) of this paper are available at https://www.medrxiv.org/content/10.1101/2020.06.10.20127225v1, first published .
Identifying and Ranking Common COVID-19 Symptoms From Tweets in Arabic: Content Analysis

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

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

Authors of this article:

Eisa Alanazi1 Author Orcid Image ;   Abdulaziz Alashaikh2 Author Orcid Image ;   Sarah Alqurashi1 Author Orcid Image ;   Aued Alanazi1 Author Orcid Image
Article Authors Cited by (16) Tweetations (13) Metrics

Journals

  1. Al-Laith A, Alenezi M. Monitoring People’s Emotions and Symptoms from Arabic Tweets during the COVID-19 Pandemic. Information 2021;12(2):86 View
  2. Jong W, Liang O, Yang C. The Exchange of Informational Support in Online Health Communities at the Onset of the COVID-19 Pandemic: Content Analysis. JMIRx Med 2021;2(3):e27485 View
  3. Alsudias L, Rayson P. Social Media Monitoring of the COVID-19 Pandemic and Influenza Epidemic With Adaptation for Informal Language in Arabic Twitter Data: Qualitative Study. JMIR Medical Informatics 2021;9(9):e27670 View
  4. Bastani P, Hakimzadeh S, Bahrami M. Designing a conceptual framework for misinformation on social media: a qualitative study on COVID-19. BMC Research Notes 2021;14(1) View
  5. Tshitangano T, Setati M, Mphekgwana P, Ramalivhana N, Matlala S. Epidemiological Characteristics of COVID-19 Inpatient Deaths during the First and Second Waves in Limpopo Province, South Africa. Journal of Respiration 2022;2(2):111 View
  6. Luo L, Wang Y, Mo D. Identifying COVID-19 Personal Health Mentions From Tweets Using Masked Attention Model. IEEE Access 2022;10:59068 View
  7. Alswedani S, Katib I, Abozinadah E, Mehmood R. Discovering Urban Governance Parameters for Online Learning in Saudi Arabia During COVID-19 Using Topic Modeling of Twitter Data. Frontiers in Sustainable Cities 2022;4 View
  8. Elsaka T, Afyouni I, Hashem I, Al Aghbari Z. Spatio-Temporal Sentiment Mining of COVID-19 Arabic Social Media. ISPRS International Journal of Geo-Information 2022;11(9):476 View
  9. Wu J, Wang L, Hua Y, Li M, Zhou L, Bates D, Yang J. Trend and Co-occurrence Network of COVID-19 Symptoms From Large-Scale Social Media Data: Infoveillance Study. Journal of Medical Internet Research 2023;25:e45419 View
  10. Hou J, Liang C, Chen P. How Socially Perceived Threat Shapes Preventive Behavior in the Context of COVID-19. Production and Operations Management 2024 View
  11. Afyouni I, Hashim I, Aghbari Z, Elsaka T, Almahmoud M, Abualigah L. Insights from the COVID-19 Pandemic: A Survey of Data Mining and Beyond. Applied Spatial Analysis and Policy 2024;17(3):1359 View
  12. Bishal M, Chowdory M, Das A, Kabir M. COVIDHealth: A novel labeled dataset and machine learning-based web application for classifying COVID-19 discourses on Twitter. Heliyon 2024;10(14):e34103 View
  13. Valades M, Montero-Torres M, Lara-Abelenda F, Carabot F, Ortega M, Álvarez-Mon M, Alvarez-Mon M. Understanding public perceptions and discussions on diseases involving chronic pain through social media: cross-sectional infodemiology study. BMC Musculoskeletal Disorders 2024;25(1) View

Books/Policy Documents

  1. Elsaka T, Afyouni I, Hashem I, AL-Aghbari Z. Discovery Science. View