ArticleBreast cancer research and treatment2016
Learning from social media: utilizing advanced data extraction techniques to understand barriers to breast cancer treatment.
Article in Breast cancer research and treatment, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Application of Artificial Intelligence Methods to Pharmacy Data for Cancer Surveillance and Epidemiology Research: A Systematic Review.JCO clinical cancer informatics · 2020Pooled it
- Secondary Use of Recorded or Self-expressed Personal Data: Consumer Health Informatics and Education in the Era of Social Media and Health Apps.Yearbook of medical informatics · 2017Pooled it
- Patient Voice and Treatment Nonadherence in Cancer Care: A Scoping Review of Sentiment Analysis.Nursing reports (Pavia, Italy) · 2026Review
- Women's Insights on Extended Adjuvant Endocrine Therapy for Breast Cancer: Qualitative Online Forums Study.Journal of medical Internet research · 2025Article
- Patient perceptions of disease burden and treatment of myasthenia gravis based on sentiment analysis of digital conversations.Scientific reports · 2024Article
- Use of sentiment analysis for capturing hospitalized cancer patients' experience from free-text comments in the Persian language.BMC medical informatics and decision making · 2023Article
- Listening to voices from multiple sources: A qualitative text analysis of the emotional experiences of women living with breast cancer in China.Frontiers in public health · 2023Article
- Digital conversations about depression among Hispanics and non-Hispanics in the US: a big-data, machine learning analysis identifies specific characteristics of depression narratives in Hispanics.Annals of general psychiatry · 2021Article
- Patients' Perspective About the Cost of Diabetes Management: An Analysis of Online Health Communities.Mayo Clinic proceedings. Innovations, quality & outcomes · 2021Article
- Analysis of Population Differences in Digital Conversations About Cancer Clinical Trials: Advanced Data Mining and Extraction Study.JMIR cancer · 2021Article
- Extending Adjuvant Endocrine Therapy for 10 Years: A Mixed-Methods Analysis of Women's Decision Making in an Online Breast Cancer Forum.Healthcare (Basel, Switzerland) · 2021Article
- Using Social Media Data to Understand Consumers' Information Needs and Emotions Regarding Cancer: Ontology-Based Data Analysis Study.Journal of medical Internet research · 2020Article
- Understanding the Public's Emotions about Cancer: Analysis of Social Media Data.International journal of environmental research and public health · 2020Article
- Exploring Strategies for Using Social Media to Self-Manage Health Care When Living With and Beyond Breast Cancer: In-Depth Qualitative Study.Journal of medical Internet research · 2020Article
- Patient Experiences and Perceptions Associated with the Use of Desiccated Thyroid Extract.Medicina (Kaunas, Lithuania) · 2020Article
- The association between social media use for health related information and compliance with breast and cervical cancer screenings.Research reports (Montgomery) · 2020Article
- The therapy is making me sick: how online portal communications between breast cancer patients and physicians indicate medication discontinuation.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- How Social Networks May Influence Cancer Patients' Situated Identity and Illness-Related Behaviors.Frontiers in public health · 2018Review
- The Bigger Picture: Picking the Right Soap Box-Is it Possible to Connect with Different Audience Targets (Practitioners and Patients) from the Same Platform?Clinics in colon and rectal surgery · 2017Review
- Talking About My Care: Detecting Mentions of Hormonal Therapy Adherence Behavior in an Online Breast Cancer Community.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.