Gaps in Public Awareness About BRCA and Genetic Testing in Prostate Cancer: Social Media Landscape Analysis
Bibliographic record
Abstract
BACKGROUND: Genetic testing, particularly for BRCA1/2, is increasingly important in prostate cancer (PCa) care, with impact on PCa management and hereditary cancer risk. However, the extent of public awareness and online discourse on social media is unknown, and presents opportunities to identify gaps and enhance population awareness and uptake of advances in PCa precision medicine. OBJECTIVE: The objective of this study was to characterize activity and engagement across multiple social media platforms (Twitter, Facebook, and YouTube) regarding BRCA and genetic testing for PCa compared with breast cancer, which has a long history of public awareness, advocacy, and prominent social media presence. METHODS: The Symplur Signals online analytics platform was used to obtain metrics for tweets about (1) #BRCA and #breastcancer, (2) #BRCA and #prostatecancer, (3) #genetictesting and #breastcancer, and (4) #genetictesting and #prostatecancer from 2016 to 2020. We examined the total number of tweets, users, and reach for each hashtag, and performed content analysis for a subset of tweets. Facebook and YouTube were queried using analogous search terms, and engagement metrics were calculated. RESULTS: During a 5-year period, there were 10,005 tweets for #BRCA and #breastcancer, versus 1008 tweets about #BRCA and #prostatecancer. There were also more tweets about #genetictesting and #breastcancer (n=1748), compared with #genetic testing and #prostatecancer (n=328). Tweets about genetic testing (12,921,954) and BRCA (75,724,795) in breast cancer also had substantially greater reach than those about PCa (1,463,777 and 4,849,905, respectively). Facebook groups and pages regarding PCa and BRCA/genetic testing had fewer average members, new members, and new posts, as well as fewer likes and followers, compared with breast cancer. Facebook videos had more engagement than YouTube videos across both PCa and breast cancer content. CONCLUSIONS: There is substantially less social media engagement about BRCA and genetic testing in PCa compared with breast cancer. This landscape analysis provides insights into strategies for leveraging social media platforms to increase public awareness about PCa germline testing, including use of Facebook to share video content and Twitter for discussions with health professionals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".