ArticleAesthetic surgery journal2025
Beyond the Posts: Analyzing Breast Implant Illness Discourse With Natural Language Processing and Deep Learning.
Article in Aesthetic surgery journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Sentiment Analysis of Autologous Breast Reconstruction Using Natural Language Processing and Deep Learning.Aesthetic surgery journal. Open forum · 2025Article
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBreast implant illness (BII) is a spectrum of symptoms some people attribute to breast implants. Although causality remains unproven, patient interest has grown significantly. Understanding patient perceptions of BII on social media is crucial because these platforms increasingly influence healthcare decisions.
objectivesThe purpose of this study is to analyze patient perceptions and emotional responses to BII on social media using Robust optimizing Bidirectional Encoder Representations from Transformers, a natural processing model trained on 124 million X posts.
methodsPosts mentioning BII from 2014 to 2023 were analyzed using 2 natural language processing models: 1 for sentiment (positive/negative) and another for emotions (fear, sadness, anger, disgust, neutral, surprise, and joy). Posts were then classified by their highest scoring emotion. The results were compared over across 2014-2018 and 2019-2023, with correlation analysis (Pearson correlation coefficient) between published implant explantation and augmentation data.
resultsThe analysis of 6099 posts over 10 years showed 75.4% were negative, with monthly averages of 50.85 peaking at 213 in March 2019. Fear and neutral emotions dominated, representing 35.9% and 35.6%, respectively. The strongest emotions were neutral and fear, with an average score of 0.293 and 0.286 per post, respectively. Fear scores increased from 0.219 (2014-2018) to 0.303 (2019-2023). Strong positive correlations (r > 0.70) existed between annual explantation rates/explantation-to-augmentation ratios and total, negative, neutral, and fear posts.
conclusionsBII discourse on X peaked in 2019 characterized predominantly by negative sentiment and fear. The strong correlation between fear/negative-based posts and explantation rates suggests social media discourse significantly influences patient decisions regarding breast implant removal.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.