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 ORCID: https://orcid.org/0000-0002-9458-9332, Al Baghal, Tarek, Macleod, John, Haworth, Claire M. A. and Davis, Oliver S. P. 2023. Epicosm -a framework for linking online social media in epidemiological cohorts. International Journal of Epidemiology 52 (3) , pp. 952-957. 10.1093/ije/dyad020

PDF - Published Version Available under License Creative Commons Attribution. Download (257kB)

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/].

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Social Sciences (Includes Criminology and Education)
Publisher:	Oxford University Press
ISSN:	0300-5771
Funders:	ESRC, MRC, EPSRC, Philip Leverhulme Prize
Date of First Compliant Deposit:	8 March 2023
Date of Acceptance:	16 February 2023
Last Modified:	13 Jul 2023 16:56
URI:	https://orca.cardiff.ac.uk/id/eprint/157548

Actions (repository staff only)

Edit Item