Abstract 1040: Voices of resilience: Analysing #MetastaticBreastCancer content on TikTok
Bibliographic record
Abstract
INTRODUCTION: Young women with metastatic breast cancer (YWMBC) have unique biopsychosocial challenges, for which they often turn to social media for information and support. The aim of this study is to understand YWMBC engagement on TikTok and analyze content about MBC. METHODS: Videos tagged with #metastaticbreastcancer on TikTok were collected in 07/24. Reviewers gathered video characteristics: username, user profile, caption, length, date posted, number of followers, likes, views, shares, comments, and presence of sponsorship. Creator demographics were noted. Explanations, communication style, themes, and topics discussed were assessed. Spearman’s correlation coefficient was calculated between variables. RESULTS: 336 English language TikToks were included. Most videos (41%) were posted in 2024, 97% were from individuals. 1% included sponsorship. Average video length was 1:46 minutes. 161 used music. Mean follower number was 61,927 (SD 350,900). Mean number of views, likes, and comments was 209,882 (SD 625,961), 15,541 (SD 134,070), and 622 (SD 3899), respectively. The majority of videos were filmed at home (56%), specifically in the bedroom. 21% were filmed in a hospital setting. Average age at diagnosis was 31.4 years old (SD 4.3). Emotion was explicitly present in 24% of videos. Topics discussed included: daily life with MBC (70%), diagnosis (42%), treatment options (38% systemic, 26% surgery and 16% radiation). Themes are addressed in Table 1. Most relevant correlations for engagement (likes, views) included display of surgical scars, explicit expression of emotions, and diagnosis story. Predictors specific to more comments included fear of recurrence, grief and motherhood. CONCLUSION: TikToks from YWMBC present personal narratives of the daily experience of MBC. They are minimally sponsored and generate higher engagement when personal content/emotions are displayed. They may represent a journaling equivalent and could potentially have a beneficial effect on patients. Citation Format: Nina Morena, Carla Herman, Jillian Schneidman, Eric Belzile, Ari N. Meguerditchian. Voices of resilience: Analysing #MetastaticBreastCancer content on TikTok [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1040.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".