Social Media–Based Cancer Education: Bibliometric and Thematic Analysis
Bibliographic record
Abstract
Background: Traditional education for patients with cancer faces challenges related to timeliness, accessibility, and a personalized approach. Social media has emerged as a novel platform for delivering cancer-related educational content, garnering growing academic interest. However, a comprehensive assessment of the current research landscape in this domain is lacking. Objective: This study aimed to identify research hotspots; trace the evolution of social media-based education for patients with cancer; and map the leading journals, institutions, and international collaboration networks in this field. Methods: A bibliometric and thematic analysis was conducted using tools, such as VOSviewer, Bibliometrix, and CiteSpace, to examine articles indexed in the Web of Science Core Collection from 2011 to 2025. The analysis explored publication trends, author and institutional collaboration networks, keyword co-occurrence, factor analysis, thematic clusters, and the evolution of disciplinary keyword categories. Results: A total of 119 publications were retrieved. The Journal of Medical Internet Research was the most productive journal in this field, publishing 13 articles (10.9%). The University of Minnesota was the most productive institution, contributing 6 publications (5.0%). The United States accounted for the largest proportion of publications (56/119, 47.1%), with 5 of the top 10 institutions based in the country. The United States also led the international collaboration network. Keyword analysis identified key research hotspots, including platform-specific information dissemination, tailored educational interventions for diverse patient populations, efforts to enhance quality of life, and challenges related to health misinformation. Thematic evolution demonstrated a shift from basic information-seeking behaviors to broader topics such as digital health and health equity, indicating a multidimensional and interdisciplinary research trajectory. Conclusions: This study represents the first bibliometric analysis of social media-based cancer education, providing actionable insights to inform digital health literacy strategies and advance patient-centered, equitable health care.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.077 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".