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

Vishal Dey; Peter Krasniak; Minh Nguyen; Clara Lee; Xia Ning

doi:10.2196/29768

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

JMIR Med Inform. 2021 Nov 29;9(11):e29768. doi: 10.2196/29768.

Authors

Vishal Dey¹, Peter Krasniak², Minh Nguyen², Clara Lee², Xia Ning^{1

2

3}

Affiliations

¹ Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, United States.
² Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States.
³ Translational Data Analytics Institute, The Ohio State University, Columbus, OH, United States.

PMID: 34847064
PMCID: PMC8669576
DOI: 10.2196/29768

Abstract

Background: A new illness can come to public attention through social media before it is medically defined, formally documented, or systematically studied. One example is a condition known as breast implant illness (BII), which has been extensively discussed on social media, although it is vaguely defined in the medical literature.

Objective: The objective of this study is to construct a data analysis pipeline to understand emerging illnesses using social media data and to apply the pipeline to understand the key attributes of BII.

Methods: We constructed a pipeline of social media data analysis using natural language processing and topic modeling. Mentions related to signs, symptoms, diseases, disorders, and medical procedures were extracted from social media data using the clinical Text Analysis and Knowledge Extraction System. We mapped the mentions to standard medical concepts and then summarized these mapped concepts as topics using latent Dirichlet allocation. Finally, we applied this pipeline to understand BII from several BII-dedicated social media sites.

Results: Our pipeline identified topics related to toxicity, cancer, and mental health issues that were highly associated with BII. Our pipeline also showed that cancers, autoimmune disorders, and mental health problems were emerging concerns associated with breast implants, based on social media discussions. Furthermore, the pipeline identified mentions such as rupture, infection, pain, and fatigue as common self-reported issues among the public, as well as concerns about toxicity from silicone implants.

Conclusions: Our study could inspire future studies on the suggested symptoms and factors of BII. Our study provides the first analysis and derived knowledge of BII from social media using natural language processing techniques and demonstrates the potential of using social media information to better understand similar emerging illnesses.

Keywords: breast implant illness; natural language processing; social media; topic modeling.

©Vishal Dey, Peter Krasniak, Minh Nguyen, Clara Lee, Xia Ning. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 29.11.2021.