Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 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
Affiliations

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.

PubMed Disclaimer

Conflict of interest statement

None declared.

References

    1. De Choudhury M, Gamon M, Counts S. et al. Predicting depression via social media. In: Seventh International AAAI Conference on Weblogs and Social Media, 8–11 July 2013. Cambridge MA: Association for the Advancement of Artificial Intelligence, 2013.
    1. McGorry PD, Ratheesh A, O'Donoghue B.. Early intervention—an implementation challenge for 21st century mental health care. JAMA Psych 2018;75:545–46. - PubMed
    1. Melcher J, Lavoie J, Hays R. et al. Digital phenotyping of student mental health during COVID-19: An observational study of 100 college students. J Am Coll Health 2021;Mar 26:1–13. - PubMed
    1. Melcher J, Hays R, Torous J.. Digital phenotyping for mental health of college students: a clinical review. Evid Based Ment Health 2020;23:161–66. - PMC - PubMed
    1. Blair J, Hsu C-Y, Qiu L. et al. Using tweets to assess mental well-being of essential workers during the covid-19 pandemic. In: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems,, 8–13 May 2021. Yokohama, Japan. New York, NY: Association for Computing Machinery, 2021, pp. 1–6.

Publication types