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

Area of Science:

• Oncology
• Natural Language Processing
• Social Media Analytics

Background:

• Early-stage breast cancer presents treatment choices like breast-conserving surgery (BCS) or mastectomy.
• Social media serves as an information source and decision-making tool for patients, necessitating awareness of online discussions.

Purpose of the Study:

• To compare sentiments and emotions in social media conversations about BCS versus mastectomy using natural language processing (NLP).

Main Methods:

• Collected and analyzed 105,231 Reddit paragraphs (2011-2021) discussing BCS or mastectomy.
• Utilized Apache Clinical Text Analysis Knowledge Extraction System for concept identification and VADER/GoEmotions for sentiment and emotion analysis.

Main Results:

• Mastectomy discussions showed increasing positive sentiment over time, while BCS discussions remained neutral.
• Mastectomy conversations generally had more positive sentiments than BCS.
• Common emotions included neutrality, gratitude, caring, approval, and optimism, with increased anger, annoyance, and disappointment noted for BCS discussions over time.

Conclusions:

• Online patient communities are vital for breast cancer therapy discussions.
• The increasing positivity of mastectomy discussions contrasts with neutral BCS discussions, mirroring national trends.
• Recognizing patient sentiments and emotions can guide patient-centric and emotionally sensitive treatment recommendations.