Perceptions in 3.6 Million Web-Based Posts of Online Communities on the Use of Cancer Immunotherapy: Data Mining...

Area of Science:

• Oncology
• Computational Linguistics
• Social Media Analysis

Background:

• Immunotherapy is a transformative cancer treatment.
• Patients increasingly use online platforms for support and information sharing.
• Limited research exists on patient perceptions of immunotherapy using social media data.

Purpose of the Study:

• To employ BERTopic modeling to analyze online cancer community perceptions of immunotherapy.
• To identify key themes and sentiments surrounding immunotherapy discussions.

Main Methods:

• Processed 3.6 million social media posts from various online platforms (Facebook, Twitter, Reddit, forums).
• Utilized natural language processing and BERTopic for topic modeling and sentiment analysis.
• Conducted thematic analysis on identified topics to understand patient perspectives.

Main Results:

• BERTopic identified 14 distinct topics related to immunotherapy perceptions.
• Sentiment analysis revealed generally positive sentiments (score ~0.3) towards immunotherapy.
• Six key themes emerged: hope, effectiveness, complementary treatments, financial/mental impact, lifestyle, and side effects.

Conclusions:

• Social media analysis using BERTopic offers insights into multifaceted patient considerations for immunotherapy.
• Identified themes can enhance physician-patient communication and shared decision-making.
• BERTopic is effective for analyzing large datasets to uncover social media perceptions.