Exploring Urological Malignancies on Pinterest: Content Analysis
Bibliographic record
Abstract
BACKGROUND: Pinterest is a visually oriented social media platform with over 250 million monthly users. Previous studies have found misinformative content on genitourinary malignancies to be broadly disseminated on YouTube; however, no study has assessed the quality of this content on Pinterest. OBJECTIVE: Our objective was to evaluate the quality, understandability, and actionability of genitourinary malignancy content on Pinterest. METHODS: We examined 540 Pinterest posts or pins, using the following search terms: "bladder cancer," "kidney cancer," "prostate cancer," and "testicular cancer." The pins were limited to English language and topic-specific content, resulting in the following exclusions: bladder (n=88), kidney (n=4), prostate (n=79), and testicular cancer (n=10), leaving 359 pins as the final analytic sample. Pinterest pins were classified based on publisher and perceived race or ethnicity. Content was assessed using 2 validated grading systems: DISCERN quality criteria and the Patient Education Materials Assessment Tool. The presence of misinformation was evaluated using a published Likert scale ranging from 1=none to 5=high. RESULTS: Overall, 359 pins with a total of 8507 repins were evaluated. The primary publisher of genitourinary malignancy pins were health and wellness groups (n=162, 45%). Across all genitourinary malignancy pins with people, only 3% (n=7) were perceived as Black. Additionally, Asian (n=2, 1%) and Latinx (n=1, 0.5%) individuals were underrepresented in all pins. Nearly 75% (n=298) of the pins had moderate- to poor-quality information. Misinformative content was apparent in 4%-26% of all genitourinary cancer pins. Understandability and actionability were poor in 55% (n=198) and 100% (n=359) of the pins, respectively. CONCLUSIONS: On Pinterest, the majority of the urological oncology patient-centric content is of low quality and lacks diversity. This widely used, yet unregulated platform has the ability to influence consumers' health knowledge and decision-making. Ultimately, this can lead to consumers making suboptimal medical decisions. Moreover, our findings demonstrate underrepresentation across many racial and ethnic groups. Efforts should be made to ensure the dissemination of diverse, high-quality, and accurate health care information to the millions of users on Pinterest and other social media platforms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".