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Epicosm-a framework for linking online social media in epidemiological cohorts.
Tanner, Alastair R; Di Cara, Nina H; Maggio, Valerio; Thomas, Richard; Boyd, Andy; Sloan, Luke; Al Baghal, Tarek; Macleod, John; Haworth, Claire M A; Davis, Oliver S P.
Afiliação
  • Tanner AR; Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
  • Di Cara NH; Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
  • Maggio V; Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
  • Thomas R; Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
  • Boyd A; Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
  • Sloan L; Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
  • Al Baghal T; Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
  • Macleod J; School of Social Sciences, Cardiff University, Cardiff, UK.
  • Haworth CMA; Institute for Social and Economic Research, University of Essex, Colchester, UK.
  • Davis OSP; Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
Int J Epidemiol ; 52(3): 952-957, 2023 06 06.
Article em En | MEDLINE | ID: mdl-36847716
ABSTRACT
MOTIVATION Social media represent an unrivalled opportunity for epidemiological cohorts to collect large amounts of high-resolution time course data on mental health. Equally, the high-quality data held by epidemiological cohorts could greatly benefit social media research as a source of ground truth for validating digital phenotyping algorithms. However, there is currently a lack of software for doing this in a secure and acceptable manner. We worked with cohort leaders and participants to co-design an open-source, robust and expandable software framework for gathering social media data in epidemiological cohorts. IMPLEMENTATION Epicosm is implemented as a Python framework that is straightforward to deploy and run inside a cohort's data safe haven. GENERAL FEATURES The software regularly gathers Tweets from a list of accounts and stores them in a database for linking to existing cohort data.

AVAILABILITY:

This open-source software is freely available at [https//dynamicgenetics.github.io/Epicosm/].
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Mídias Sociais Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Mídias Sociais Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article