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. 2023 Jun 6;52(3):952-957.
doi: 10.1093/ije/dyad020.

Epicosm-a framework for linking online social media in epidemiological cohorts

Alastair R Tanner  1 , Nina H Di Cara  1   2 , Valerio Maggio  1   2 , Richard Thomas  2 , Andy Boyd  2 , Luke Sloan  3 , Tarek Al Baghal  4 , John Macleod  2 , Claire M A Haworth  5   6 , Oliver S P Davis  1   2   6
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Epicosm-a framework for linking online social media in epidemiological cohorts

Alastair R Tanner et al. Int J Epidemiol. .

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/].

Keywords: ALSPAC; Big Data; Social media; cohort studies; data linkage; data science; epidemiology; longitudinal studies; mental health; wellbeing.

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Conflict of interest statement

None declared.

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