Cargando…
Using language in social media posts to study the network dynamics of depression longitudinally
Network theory of mental illness posits that causal interactions between symptoms give rise to mental health disorders. Increasing evidence suggests that depression network connectivity may be a risk factor for transitioning and sustaining a depressive state. Here we analysed social media (Twitter)...
Autores principales: | Kelley, Sean W., Gillan, Claire M. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8847554/ https://www.ncbi.nlm.nih.gov/pubmed/35169166 http://dx.doi.org/10.1038/s41467-022-28513-3 |
Ejemplares similares
-
Dataset of depressive posts in Russian language collected from social media
por: Narynov, Sergazy, et al.
Publicado: (2020) -
Machine learning of language use on Twitter reveals weak and non-specific predictions
por: Kelley, Sean W., et al.
Publicado: (2022) -
Elevated emotion network connectivity is associated with fluctuations in depression
por: Kelley, Sean W., et al.
Publicado: (2023) -
Detection of Depression Severity Using Bengali Social Media Posts on Mental Health: Study Using Natural Language Processing Techniques
por: Kabir, Muhammad Khubayeeb, et al.
Publicado: (2022) -
Longitudinal data collection to follow social network and language development dynamics at preschool
por: Dai, Sicheng, et al.
Publicado: (2022)