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

Natural Language Processing (692) Infodemiology and Infoveillance (1349) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) Machine Learning (1465) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) Infoveillance and Social Listening (209) Ontologies, Classifications, and Coding (239)

Published on in Vol 9, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29768, first published .
A Pipeline to Understand Emerging Illness Via Social Media Data Analysis: Case Study on Breast Implant Illness

A Pipeline to Understand Emerging Illness Via Social Media Data Analysis: Case Study on Breast Implant Illness

A Pipeline to Understand Emerging Illness Via Social Media Data Analysis: Case Study on Breast Implant Illness

Authors of this article:

Vishal Dey1 Author Orcid Image ;   Peter Krasniak2 Author Orcid Image ;   Minh Nguyen2 Author Orcid Image ;   Clara Lee2 Author Orcid Image ;   Xia Ning1, 2, 3 Author Orcid Image
Article Authors Cited by (3) Tweetations (8) Metrics

Journals

  1. Quinn V, Pearson S, Huynh A, Nicholls K, Barnes K, Faasse K. The influence of video-based social modelling on the nocebo effect. Journal of Psychosomatic Research 2023;165:111136 View
  2. de Faria Castro Fleury E. Breast silicone implants’ pericapsular impairment: current underdiagnosed status. Frontiers in Surgery 2023;10 View
  3. Hu S, Liu J, Cornacchi S, Klassen A, Pusic A, Kaur M. Extracting big data from the internet to support the development of a new patient-reported outcome measure for breast implant illness: a proof of concept study. Quality of Life Research 2024;33(7):1975 View