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) Natural Language Processing (692) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) Outbreak and Pandemic Preparedness and Management (1727)

Published on in Vol 23, No 1 (2021): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25314, first published .
Toward Using Twitter for Tracking COVID-19: A Natural Language Processing Pipeline and Exploratory Data Set

Toward Using Twitter for Tracking COVID-19: A Natural Language Processing Pipeline and Exploratory Data Set

Toward Using Twitter for Tracking COVID-19: A Natural Language Processing Pipeline and Exploratory Data Set

Authors of this article:

Ari Z Klein1 Author Orcid Image ;   Arjun Magge1 Author Orcid Image ;   Karen O'Connor1 Author Orcid Image ;   Jesus Ivan Flores Amaro1 Author Orcid Image ;   Davy Weissenbacher1 Author Orcid Image ;   Graciela Gonzalez Hernandez1 Author Orcid Image
Article Authors Cited by (40) Tweetations (22) Metrics

Journals

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  15. James P, Jonczyk J, Smith L, Harris N, Komar T, Bell D, Ranjan R. Realizing Smart City Infrastructure at Scale, in the Wild: A Case Study. Frontiers in Sustainable Cities 2022;4 View
  16. Durazzi F, Pichard F, Remondini D, Salathé M. Dynamics of social media behavior before and after SARS-CoV-2 infection. Frontiers in Public Health 2023;10 View
  17. Álvarez-Carmona M, Aranda R, Rodríguez-González A, Pellegrin L, Carlos H. Classifying the Mexican epidemiological semaphore colour from the Covid-19 text Spanish news. Journal of Information Science 2024;50(3):568 View
  18. Ricard B, Hassanpour S. Deep Learning for Identification of Alcohol-Related Content on Social Media (Reddit and Twitter): Exploratory Analysis of Alcohol-Related Outcomes. Journal of Medical Internet Research 2021;23(9):e27314 View
  19. Hua Y, Jiang H, Lin S, Yang J, Plasek J, Bates D, Zhou L. Using Twitter data to understand public perceptions of approved versus off-label use for COVID-19-related medications. Journal of the American Medical Informatics Association 2022;29(10):1668 View
  20. Gatto J, Seegmiller P, Johnston G, Preum S. Identifying the Perceived Severity of Patient-Generated Telemedical Queries Regarding COVID: Developing and Evaluating a Transfer Learning–Based Solution. JMIR Medical Informatics 2022;10(9):e37770 View
  21. Lu G, Businger M, Dollfus C, Wozniak T, Fleck M, Heroth T, Lock I, Lipenkova J. Agenda-Setting for COVID-19: A Study of Large-Scale Economic News Coverage Using Natural Language Processing. International Journal of Data Science and Analytics 2023;15(3):291 View
  22. Wang A, Dara R, Yousefinaghani S, Maier E, Sharif S. A Review of Social Media Data Utilization for the Prediction of Disease Outbreaks and Understanding Public Perception. Big Data and Cognitive Computing 2023;7(2):72 View
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  24. Dolatabadi E, Moyano D, Bales M, Spasojevic S, Bhambhoria R, Bhatti J, Debnath S, Hoell N, Li X, Leng C, Nanda S, Saab J, Sahak E, Sie F, Uppal S, Vadlamudi N, Vladimirova A, Yakimovich A, Yang X, Kocak S, Cheung A. Using Social Media to Help Understand Patient-Reported Health Outcomes of Post–COVID-19 Condition: Natural Language Processing Approach. Journal of Medical Internet Research 2023;25:e45767 View
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  26. Suzuki N, Takumi Y. A Study of the Trends of Pollen Dispersal and Hay Fever Symptoms Using Twitter. Nippon Jibiinkoka Tokeibugeka Gakkai Kaiho(Tokyo) 2023;126(6):777 View
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