115 #Breastfeeding: A content analysis of breastfeeding information on TikTok
Bibliographic record
Abstract
Abstract Background Breastfeeding offers well-documented benefits for both mothers and infants, yet breastfeeding rates remain below government targets, particularly among adolescents and individuals with lower levels of education. Adolescents have identified social media as a potential avenue for breastfeeding education. With TikTok emerging as the most popular platform among minors worldwide, it was selected as our focus. Despite its rising popularity, prior research suggests that social media often lacks sufficient informational content. Objectives This study analyzed breastfeeding-related content on TikTok, by addressing two key questions: 1) Who are the individuals creating videos with the hashtag #breastfeeding? 2) What themes and messages are conveyed in these videos? Design/Methods Our research design utilized a retrospective review of breastfeeding content available on TikTok. Data was collected from November 25, 2024 to December 6th, 2024. As the videos were posted publicly to the social media application, consent was implied. A content analysis was performed using two search phases. In both phases #breastfeeding was entered into the TikTok search bar. In the first phase we collected the usernames of the accounts which were provided from this TikTok search. In the second phase, we focused on the 'Hashtags' section of TikTok's search to curate posts containing the hashtag #breastfeeding. In both phases of the analysis, content was coded based on six variables: account qualification, date accessed, date of last post, video theme(s), number of followers, and number of likes. Results An analysis of the top 100 videos under the hashtag #breastfeeding revealed key trends in content type and origin. Only 10% (10/100) of these videos were classified as educational or informational. The search for #breastfeeding on TikTok yielded 54 accounts or “users.” Among these, 42 accounts (N=42, f=77.8%) were found to primarily repost videos originally created by others. Four accounts (f=7.4%) were classified as educational or informational, with only half of these (50%) managed by professionals or experts. Notably, 40 of the 54 accounts (f=74.1%) utilized breastfeeding as a means to share nudity or explicit content, with nearly all of these (N=39, f=72.2%) reposting videos from other users. the app suggests topics to search based on user activity. When the hashtags #breastfeeding and "breastfeeding" were searched using a newly created account named Research4867, the app provided 10 search recommendations. All initial suggestions were inappropriate and sexual in nature (N=10, f=100%). Our findings reveal a prevalence of sensationalized content and a significant gap in educational material. Alarmingly, the platform is being used to share nudity and explicit content, a trend exacerbated by TikTok’s recommended searches. Conclusion These findings underscore the urgent need to safeguard TikTok from becoming a medium that perpetuates harmful stereotypes and to enhance its potential as a source of accurate breastfeeding education. While breastfeeding is a natural and essential act, we are alarmed to note it is being used as an avenue for posting nudity and explicit content on the social media forum. This misrepresents the true purpose of breastfeeding while also contributing to the stigma surrounding it.
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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 | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".