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Learning from social media: utilizing advanced data extraction techniques to understand barriers to breast cancer treatment

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Abstract

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 online, candid discussions about breast cancer. 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. 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 %), healthcare perceptions (9 %), treatment processes/duration (7 %), and relationships (7 %). Black and Hispanic (vs. white) users more frequently reported barriers related to healthcare perceptions, beliefs, and pre-diagnosis/diagnosis organizational challenges and fewer emotional barriers. 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 online. Future research should focus on how to further employ and learn from this type of social intelligence research across all medical disciplines.

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Acknowledgments

We thank Patrick Furey at ConsumerSphere for his thoughtful assistance with this analysis.

Funders

The funders for this study are American Cancer Society (RAF), Susan G. Komen (RAF), K24CA181510 (NLK) from the National Cancer Institute, Fundacao para a Ciencia e Tecnologia [HMSP-ICS/0004/2011, Career Development Award, (IVL)].

Author information

Authors and Affiliations

  1. Department of Medical Oncology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA

    Rachel A. Freedman & Ines Vaz-Luis

  2. Center for Community-Based Research, Dana-Farber Cancer Institute, Boston, MA, USA

    Kasisomayajula Viswanath

  3. Department of Society and Behavioral Sciences, Harvard School of Public Health, Boston, MA, USA

    Kasisomayajula Viswanath

  4. Department of Health Care Policy, Harvard Medical School, Boston, MA, USA

    Nancy L. Keating

  5. Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    Nancy L. Keating

Authors
  1. Rachel A. Freedman
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  2. Kasisomayajula Viswanath
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  3. Ines Vaz-Luis
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  4. Nancy L. Keating
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Corresponding author

Correspondence to Rachel A. Freedman.

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Conflicts of interest

Rachel A Freedman has no relevant conflicts; research funding (to institution only) was received from Eisai Co., Ltd, and Genentech, Puma Biotechnology. Kasisomayajula Viswanath, Ines Vaz-Luis, and Nancy L. Keating have no conflicts to disclose.

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Freedman, R.A., Viswanath, K., Vaz-Luis, I. et al. Learning from social media: utilizing advanced data extraction techniques to understand barriers to breast cancer treatment. Breast Cancer Res Treat 158, 395–405 (2016). https://doi.org/10.1007/s10549-016-3872-2

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  • DOI: https://doi.org/10.1007/s10549-016-3872-2

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