Examining the Influence on Perceptions of Endometriosis via Analysis of Social Media Posts: Cross-sectional Study
Notice bibliographique
Résumé
BACKGROUND: Social media platforms, such as Facebook and Instagram, are increasingly being used to share health-related information by "influencers," regular users, and institutions alike. While patients may benefit in various ways from these interactions, little is known about the types of endometriosis-related information published on social media. As digital opinion leaders influence the perceptions of their followers, physicians need to be aware about ideas and beliefs that are available online, in order to address possible misconceptions and provide optimal patient care. OBJECTIVE: The aim of this study was to identify and analyze frequent endometriosis-related discussion topics on social media in order to offer caregivers insight into commonly discussed subject matter and aspects. METHODS: We performed a systematic search using predefined parameters. Using the term "endometriosis" in Facebook's search function and a social media search engine, a list of Facebook pages was generated. A list of Instagram accounts was generated using the terms "endometriosis" and "endo" in Instagram's search function. Pages and accounts in English with 5000 or more followers or likes were included. Nonpublic, unrelated, or inactive pages and accounts were excluded. For each account, the most recent 10 posts were identified and categorized by two independent examiners using qualitative content analysis. User engagement was calculated using the numbers of interactions (ie, shares, likes, and comments) for each post, stratified by the number of followers. RESULTS: A total of 39 Facebook pages and 43 Instagram accounts with approximately 1.4 million followers were identified. Hospitals and medical centers made up 15% (6/39) of the Facebook pages and 5% (2/43) of the Instagram accounts. Top accounts had up to 111,600 (Facebook) and 41,400 (Instagram) followers. A total of 820 posts were analyzed. On Facebook, most posts were categorized as "awareness" (101/390, 25.9% of posts), "education and research" (71/390, 18.2%), and "promotion" (64/390, 16.4%). On Instagram, the top categories were "inspiration and support" (120/430, 27.9% of posts), "awareness" (72/430, 16.7%), and "personal story" (72/430, 16.7%). The frequency of most categories differed significantly between platforms. User engagement was higher on Instagram than on Facebook (3.20% vs 0.97% of followers per post). On Instagram, the highest percentage of users engaged with posts categorized as "humor" (mean 4.19%, SD 4.53%), "personal story" (mean 3.02%, SD 4.95%), and "inspiration and support" (mean 2.83%, SD 3.08%). On Facebook, posts in the categories "awareness" (mean 2.05%, SD 15.56%), "humor" (mean 0.91%, SD 1.07%), and "inspiration and support" (mean 0.56%, SD 1.37%) induced the most user engagement. Posts made by hospitals and medical centers generated higher user engagement than posts by regular accounts on Facebook (mean 1.44%, SD 1.11% vs mean 0.88%, SD 2.71% of followers per post) and Instagram (mean 3.33%, SD 1.21% vs mean 3.19%, SD 2.52% of followers per post). CONCLUSIONS: Facebook and Instagram are widely used to share endometriosis-related information among a large number of users. Most posts offer inspiration or support, spread awareness about the disease, or cover personal issues. Followers mostly engage with posts with a humoristic, supportive, and awareness-generating nature. Health care providers should be aware about the topics discussed online, as this may lead to an increased understanding of the needs and demands of digitally proficient patients with endometriosis.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,009 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 tête enseignante, 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 ».