Learning from Social Media: Utilizing Advanced Data Extraction Techniques to Understand Barriers to Breast Cancer Treatment - Freedman, Rachel A. (Breast Cancer Res Treat, 2016)
PURPOSE: Past examinations of breast cancer treatment barriers have typically included registry, claims-based and smaller survey studies. We examined treatment barriers using a novel, comprehensive, social media analysis of on-line, candid discussions about breast cancer. METHODS: Using an innovative toolset to search postings on social networks, message boards, patient communities, and topical sites, we performed a large-scale qualitative analysis. We examined the sentiments and barriers expressed about breast cancer treatments by internet users during 1 year (2/1/14-1/31/15). We categorized posts based on thematic patterns and examined trends in discussions by race/ethnicity (white/black/Hispanic) when this information was available. RESULTS: We identified 1,024,041 unique posts related to breast cancer treatment. Overall, 57% of posts expressed negative sentiments. Using machine learning software, we assigned treatment barriers for 387,238 posts (38%). Barriers included emotional (23% of posts), preferences and spiritual/religious beliefs (21%), physical (18%), resource (15%), health care perceptions (9%), treatment processes/duration (7%), and relationships (7%). Black and Hispanic (vs. white) users more frequently reported barriers related to health care perceptions, beliefs, and pre-diagnosis/diagnosis organizational challenges and fewer emotional barriers. CONCLUSIONS: Using a novel analysis of diverse social media users, we observed numerous breast cancer treatment barriers that differed by race/ethnicity. Social media is a powerful tool, allowing use of real world data for qualitative research, capitalizing on the rich discussions occurring spontaneously on-line. Future research should focus on how to further employ and learn from this type of social intelligence research across all medical disciplines.