115 #Breastfeeding: A content analysis of breastfeeding information on TikTok
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,005 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».