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

Breast Cancer (290) Peer-to-Peer Support and Online Communities (797) Natural Language Processing (917) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1139) Surgical Treatment Planning (19) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1911) Patient Education for Cancer (227)

Published on in Vol 11 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/52886, first published .
Exploring the Social Media Discussion of Breast Cancer Treatment Choices: Quantitative Natural Language Processing Study

Exploring the Social Media Discussion of Breast Cancer Treatment Choices: Quantitative Natural Language Processing Study

Exploring the Social Media Discussion of Breast Cancer Treatment Choices: Quantitative Natural Language Processing Study

Authors of this article:

Daphna Y Spiegel1 Author Orcid Image ;   Isabel D Friesner2, 3, 4 Author Orcid Image ;   William Zhang2, 3, 4 Author Orcid Image ;   Travis Zack2, 4, 5 Author Orcid Image ;   Gianna Yan2, 3, 4 Author Orcid Image ;   Julia Willcox1 Author Orcid Image ;   Nicolas Prionas2 Author Orcid Image ;   Lisa Singer2 Author Orcid Image ;   Catherine Park2 Author Orcid Image ;   Julian C Hong2, 3, 4 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Jiang J, Agarwal T, Higgins M, Roy M, Adams M, Leslie S, Baurle E, Rojas K, Tevis S. Identifying resource needs of patients with breast cancer by examining alignment of Reddit post content with BREAST-Q domains. The American Journal of Surgery 2025;247:116486 View
  2. Benson R, Kenny C, Ashraf Ganjouei A, Zhao M, Darawsheh R, Qian A, Hong J. Large Language Models in Population Oncology: A Contemporary Review on the Use of Large Language Models to Support Data Collection, Aggregation, and Analysis in Cancer Care and Research. JCO Clinical Cancer Informatics 2025;(9) View