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

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
  4. Blount T, Gerhold C, Bailey V, Sweeney M. An Objective Analysis of the Quality and Readability of Online Information on Breast Implant Illness. Cureus 2025 View
  5. Fijany A, Holan C, Bishay A, Boctor M, Montorfano L, Mubang R, Vijayasekaran A, Martinez-Jorge J, Harless C, Thayer W, Connor L, Lineaweaver W, Slater E. Beyond the Posts: Analyzing Breast Implant Illness Discourse With Natural Language Processing and Deep Learning. Aesthetic Surgery Journal 2025;45(7):745 View
  6. Kent C, Holch P, Gough B, Wyld L, Jones G. Experiences of women who self-report Breast Implant Illness (BII): a qualitative evidence synthesis. Health Psychology Review 2025;19(3):659 View
  7. Al-Garadi M, LeNoue-Newton M, Matheny M, McPheeters M, Whitaker J, Deere J, McLemore M, Westerman D, Khan M, Hernández-Muñoz J, Wang X, Kuzucan A, Desai R, Reeves R. Automated Extraction of Mortality Information From Publicly Available Sources Using Large Language Models: Development and Evaluation Study. Journal of Medical Internet Research 2025;27:e71113 View