Health Topics on Facebook Groups: Content Analysis of Posts in Multiple Sclerosis Communities
Notice bibliographique
Résumé
BACKGROUND: Social network sites (SNSs) are being increasingly used to exchange health information between patients and practitioners, pharmaceutical companies, and research centers. Research contributions have explored the contents of such exchanges discussed online. They have categorized the topics discussed and explored the engagement levels of these discussions. OBJECTIVE: This research aimed at investigating the potential role of SNSs in health care. Specifically it provides an information-clustering analysis of the health information available on SNSs and develops a research design that allows an investigation of this information in enhancing health care research and delivery. In addition, this research aims at testing whether SNSs are valid tools for sharing drug-related information by patients. METHODS: This research is based on a specific chronic disease: multiple sclerosis. We searched Facebook to identify and research the social media groups related to this condition. The analysis was restricted to public groups for privacy concerns. We created a database by downloading posts from two main groups (in the English language). Subsequently, we performed a content analysis and statistical analysis; this allowed us to explore the differences between categories, their engagement levels, and the types of posts shared. The mean level of engagement for each topic was analyzed using a 1-way analysis of variance. RESULTS: From a sample of 7029 posts, initial results showed that there were 8 information categories that resonated (percentage of times the topic appears in our sample) with those who post on Facebook: information and awareness (4923/7029, 70.04%), event advertising and petitions (365/7029, 5.19%), fundraising (354/7029, 5.04%), patient support (217/7029, 3.09%), drug discussion (144/7029, 2.05%), clinical trials and research studies (59/7029, 0.84%), product and drug advertising (48/7029, 0.68%), and other (919/7029, 13.07%). Initial analysis showed that comments and likes (as measures of engagement level) are the most frequent indicators and measures of level of engagement. Our results show a high engagement level (in terms of views, likes, comments, etc) for patient support and information and awareness. In addition, although drug discussion had a low resonance, it had an unexpected highly engagement level which we found worthy of further exploration. CONCLUSIONS: SNSs have become important tools for patients and health care practitioners to share or seek information. We identified the type of information shared and how the public reacted to it. Our research confirmed that the topics discussed in social media related to specific diseases such as multiple sclerosis are similar to the information categories observed by other researchers. We unexpectedly found other categories such as drug discussion. These and other results of our study enhance our understanding of how content is disseminated and perceived within a specific disease-based community. We concluded that this information has useful implications in the design of prevention campaigns, educational programs, and chronic disease management.
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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 | 0,015 | 0,043 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 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 ».