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Background: A new illness could first come to the public attention oversocial media before it is medically defined, formally documented orsystematically studied. One example is a phenomenon known as breast implantillness (BII) that has been extensively discussed on social media, thoughvaguely defined in medical literature. Objectives: The objective of this studyis to construct a data analysis pipeline to understand emerging illness usingsocial media data, and to apply the pipeline to understand key attributes ofBII. Methods: We conducted a pipeline of social media data analysis usingNatural Language Processing (NLP) and topic modeling. We extracted mentionsrelated to signs/symptoms, diseases/disorders and medical procedures using theClinical Text Analysis and Knowledge Extraction System (cTAKES) from socialmedia data. We mapped the mentions to standard medical concepts. We summarizedmapped concepts to topics using Latent Dirichlet Allocation (LDA). Finally, weapplied this pipeline to understand BII from several BII-dedicated social mediasites. Results: Our pipeline identified topics related to toxicity, cancer andmental health issues that are highly associated with BII. Our pipeline alsoshows that cancers, autoimmune disorders and mental health problems areemerging concerns associated with breast implants based on social mediadiscussions. The pipeline also identified mentions such as rupture, infection,pain and fatigue as common self-reported issues among the public, as well astoxicity from silicone implants. Conclusions: Our study could inspire futurework studying the suggested symptoms and factors of BII. Our study provides thefirst analysis and derived knowledge of BII from social media using NLPtechniques, and demonstrates the potential of using social media information tobetter understand similar emerging illnesses.

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