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

JMIR Theme Issue: COVID-19 Special Issue (2452) Natural Language Processing (693) Machine Learning (1465) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) Infectious Diseases (non-STD/STI) (1575) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517) Infodemiology and Infoveillance (1349)

Published on in Vol 24, No 2 (2022): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/30397, first published .
Monitoring COVID-19 on Social Media: Development of an End-to-End Natural Language Processing Pipeline Using a Novel Triage and Diagnosis Approach

Monitoring COVID-19 on Social Media: Development of an End-to-End Natural Language Processing Pipeline Using a Novel Triage and Diagnosis Approach

Monitoring COVID-19 on Social Media: Development of an End-to-End Natural Language Processing Pipeline Using a Novel Triage and Diagnosis Approach

Authors of this article:

Abul Hasan1 Author Orcid Image ;   Mark Levene1 Author Orcid Image ;   David Weston1 Author Orcid Image ;   Renate Fromson2 Author Orcid Image ;   Nicolas Koslover2 Author Orcid Image ;   Tamara Levene2 Author Orcid Image
Article Authors Cited by (6) Tweetations (13) Metrics

Journals

  1. Jiang H, Castellanos A, Castillo A, Gomes P, Li J, VanderMeer D. Nurses’ Work Concerns and Disenchantment During the COVID-19 Pandemic: Machine Learning Analysis of Web-Based Discussions. JMIR Nursing 2023;6:e40676 View
  2. Neely S, Hao F. Diagnosis Disclosure and Peer-to-Peer Information Seeking Among COVID-19–Infected Social Media Users: Survey of US-Based Adults. JMIR Formative Research 2023;7:e48581 View
  3. White B, Gombert A, Nguyen T, Yau B, Ishizumi A, Kirchner L, León A, Wilson H, Jaramillo-Gutierrez G, Cerquides J, D’Agostino M, Salvi C, Sreenath R, Rambaud K, Samhouri D, Briand S, Purnat T. Using Machine Learning Technology (Early Artificial Intelligence–Supported Response With Social Listening Platform) to Enhance Digital Social Understanding for the COVID-19 Infodemic: Development and Implementation Study. JMIR Infodemiology 2023;3:e47317 View
  4. Cimiano P, Collins B, De Vuono M, Escudier T, Gottowik J, Hartung M, Leddin M, Neupane B, Rodriguez-Esteban R, Schmidt A, Starke-Knäusel C, Voorhaar M, Wieckowski K. Patient listening on social media for patient-focused drug development: a synthesis of considerations from patients, industry and regulators. Frontiers in Medicine 2024;11 View
  5. Almazroi A, Ayub N, Umer M. Enhancing aspect-based multi-labeling with ensemble learning for ethical logistics. PLOS ONE 2024;19(5):e0295248 View
  6. Azam M, Anwaar F, Khan A, Anwar M, Ghani H, Eisa T, Abdelmaboud A. A hybrid contextual framework to predict severity of infectious disease: COVID-19 case study. Egyptian Informatics Journal 2024;27:100508 View