Seeking Support on Facebook: A Content Analysis of Breast Cancer Groups
Open accessAbstract
BACKGROUND: Social network sites have been growing in popularity across broad segments of Internet users, and are a convenient means to exchange information and support. Research on their use for health-related purposes is limited. OBJECTIVE: This study aimed to characterize the purpose, use, and creators of Facebook groups related to breast cancer. METHODS: We searched Facebook (www.Facebook.com) using the term breast cancer. We restricted our analysis to groups that were related to breast cancer, operated in English, and were publicly available. Two of us independently extracted information on the administrator and purpose of the group, as well as the number of user-generated contributions. We developed a coding scheme to guide content analysis. RESULTS: We found 620 breast cancer groups on Facebook containing a total of 1,090,397 members. The groups were created for fundraising (277/620, 44.7%), awareness (236, 38.1%), product or service promotion related to fundraising or awareness (61, 9%), or patient/caregiver support (46, 7%). The awareness groups as a whole contained by far the most members (n = 957,289). The majority of groups (532, 85.8%) had 25 wall posts or fewer. The support oriented groups, 47% (27/57) of which were established by high school or college students, were associated with the greatest number of user-generated contributions. CONCLUSIONS: Facebook groups have become a popular tool for awareness-raising, fundraising, and support-seeking related to breast cancer attracting over one million users. Given their popularity and reach, further research is warranted to explore the implications of social network sites as a health resource across various health conditions, cultures, ages, and socioeconomic groups.
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Online health communities are the most robust social media sources for peer-generated health information.
“Online health communities (OHCs)—Internet-based discussion forums where individuals converse with one another about health topics—are the most robust social media sources for peer-generated health information.[9–12]”
From Perceived healthcare provider reactions to patient and caregiver use of online health communities (2014) · cites this paper as [10]
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- Authors
- Bender, J. L., Jimenez-Marroquin, M.-C., & Jadad, A. R.
- Affiliations
- University Health Network (CA)
- Corresponding author
- Bender, Jacqueline L
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2011-02-04
- Volume
- 13
- Issue
- 1
- Pages
- e16
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC3221337
- OpenAlex
- W2095995897
- Cited by
- 438
- References
- 34
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 16 · 2023: 21 · 2022: 16 · 2021: 41 · 2020: 38 · 2019: 39 · 2018: 37 · 2017: 28 · 2016: 48 · 2015: 25 · 2014: 38 · 2013: 41 · 2012: 27
- FWCI
- 36.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- University of Toronto
- Topics
- Social Sciences › Health › Social Media in Health Education
- Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
- Social Sciences › Communication › Social Media and Politics
- Keywords
- breast cancer, Facebook groups, social network sites, online support, patient support, fundraising, awareness raising, user-generated content, health information seeking
- MeSH terms
- Breast Neoplasms, Female, Fund Raising, Health Promotion, Humans, Peer Group, Social Support, Internet
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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