Exploring the Social Media Discussion of Breast Cancer Treatment Choices: Quantitative Natural Language Processing Study
Bibliographic record
Abstract
Background: Early-stage breast cancer has the complex challenge of carrying a favorable prognosis with multiple treatment options, including breast-conserving surgery (BCS) or mastectomy. Social media is increasingly used as a source of information and as a decision tool for patients, and awareness of these conversations is important for patient counseling. Objective: The goal of this study was to compare sentiments and associated emotions in social media discussions surrounding BCS and mastectomy using natural language processing (NLP). Methods: Reddit posts and comments from the Reddit subreddit r/breastcancer and associated metadata were collected using pushshift.io. Overall, 105,231 paragraphs across 59,416 posts and comments from 2011 to 2021 were collected and analyzed. Paragraphs were processed through the Apache Clinical Text Analysis Knowledge Extraction System and identified as discussing BCS or mastectomy based on physician-defined Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) concepts. Paragraphs were analyzed with a VADER (Valence Aware Dictionary for Sentiment Reasoning) compound sentiment score (ranging from -1 to 1, corresponding to negativity or positivity) and GoEmotions scores (0-1) corresponding to the intensity of 27 different emotions and neutrality. Results: Of the 105,231 paragraphs, there were 7306 (6.94% of those analyzed) paragraphs mentioning BCS and mastectomy (2729 and 5476, respectively). Discussion of both increased over time, with BCS outpacing mastectomy. The median sentiment score for all discussions analyzed in aggregate became more positive over time. In specific analyses by topic, positive sentiments for discussions with mastectomy mentions increased over time; however, discussions with BCS-specific mentions did not show a similar trend and remained overall neutral. Compared to BCS, conversations about mastectomy tended to have more positive sentiments. The most commonly identified emotions included neutrality, gratitude, caring, approval, and optimism. Anger, annoyance, disappointment, disgust, and joy increased for BCS over time. Conclusions: Patients are increasingly participating in breast cancer therapy discussions with a web-based community. While discussions surrounding mastectomy became increasingly positive, BCS discussions did not show the same trend. This mirrors national clinical trends in the United States, with the increasing use of mastectomy over BCS in early-stage breast cancer. Recognizing sentiments and emotions surrounding the decision-making process can facilitate patient-centric and emotionally sensitive treatment recommendations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".