Mapping Individual Differences on the Internet: Case Study of the Type 1 Diabetes Community
Notice bibliographique
Résumé
BACKGROUND: Social media platforms, such as Twitter, are increasingly popular among communities of people with chronic conditions, including those with type 1 diabetes (T1D). There is some evidence that social media confers emotional and health-related benefits to people with T1D, including emotional support and practical information regarding health maintenance. Research on social media has primarily relied on self-reports of web-based behavior and qualitative assessment of web-based content, which can be expensive and time-consuming. Meanwhile, recent advances in natural language processing have allowed for large-scale assessment of social media behavior. OBJECTIVE: This study attempts to document the major themes of Twitter posts using a natural language processing method to identify topics of interest in the T1D web-based community. We also seek to map social relations on Twitter as they relate to these topics of interest, to determine whether Twitter users in the T1D community post in "echo chambers," which reflect their own topics back to them, or whether users typically see a mix of topics on the internet. METHODS: Through Twitter scraping, we gathered a data set of 691,691 tweets from 8557 accounts, spanning a date range from 2008 to 2020, which includes people with T1D, their caregivers, health practitioners, and advocates. Tweet content was analyzed for sentiment and topic, using Latent Dirichlet Allocation. We used social network analysis to examine the degree to which identified topics are siloed within specific groups or disseminated through the broader T1D web-based community. RESULTS: Tweets were, on average, positive in sentiment. Through topic modeling, we identified 6 broad-bandwidth topics, ranging from clinical to advocacy to daily management to emotional health, which can inform researchers and practitioners interested in the needs of people with T1D. These analyses also replicate prior work using machine learning methods to map social behavior on the internet. We extend these results through social network analysis, indicating that users are likely to see a mix of these topics discussed by the accounts they follow. CONCLUSIONS: Twitter communities are sources of information for people with T1D and members related to that community. Topics identified reveal key concerns of the T1D community and may be useful to practitioners and researchers alike. The methods used are efficient (low cost) while providing researchers with enormous amounts of data. We provide code to facilitate the use of these methods with other populations.
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 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 | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| 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 ».