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/].
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/].
| Original language | English |
|---|---|
| Article number | dyad020 |
| Pages (from-to) | 952-957 |
| Number of pages | 6 |
| Journal | International Journal of Epidemiology |
| Volume | 52 |
| Issue number | 3 |
| Early online date | 27 Feb 2023 |
| DOIs | |
| Publication status | Published - 1 Jun 2023 |
Bibliographical note
Funding Information:This project is funded by CLOSER [ www.closer.ac.uk ], whose mission is to maximize the use, value and impact of longitudinal studies. CLOSER was funded by the Economic and Social Research Council (ESRC) and the Medical Research Council (MRC) between 2012 and 2017. Its initial 5-year grant has since been extended to March 2021 by the ESRC (grant reference: ES/K000357/1). The funders took no role in the design, execution, analysis or interpretation of the data or in the writing up of the findings. The UK Medical Research Council and Wellcome Trust (Grant ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. This publication is the work of the authors, and A.R.T. and O.S.P.D. will serve as guarantors for the contents of this paper. A comprehensive list of ALSPAC grant funding is available on the ALSPAC website [ http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf ]. The work was supported by the UK Medical Research Council through the ALSPAC and Born In Bradford Mental Health Pathfinder award (grant reference: MC_PC_17210), and in part by the UK Medical Research Council Integrative Epidemiology Unit at the University of Bristol (Grant ref: MC_UU_12013/1). O.S.P.D. and C.M.A.H. .are funded by the Alan Turing Institute under the EPSRC grant EP/N510129/1. C.M.A.H. is supported by a Philip Leverhulme Prize. Acknowledgements
Funding Information:
A.R.T. would like to thank Chris Edsall, Christopher Woods and the research software engineering team at the University of Bristol. We are extremely grateful to all the ALSPAC families who took part in the proof-of-principle study, the midwives for their help in originally recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. This project is funded by CLOSER [www.closer.ac.uk], whose mission is to maximize the use, value and impact of longitudinal studies. CLOSER was funded by the Economic and Social Research Council (ESRC) and the Medical Research Council (MRC) between 2012 and 2017. Its initial 5-year grant has since been extended to March 2021 by the ESRC (grant reference: ES/K000357/1). The funders took no role in the design, execution, analysis or interpretation of the data or in the writing up of the findings. The UK Medical Research Council and Wellcome Trust (Grant ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. This publication is the work of the authors, and A.R.T. and O.S.P.D. will serve as guarantors for the contents of this paper. A comprehensive list of ALSPAC grant funding is available on the ALSPAC website [http://www.bristol.ac.uk/alspac/external/documents/grant-acknowl edgements.pdf]. The work was supported by the UK Medical Research Council through the ALSPAC and Born In Bradford Mental Health Pathfinder award (grant reference: MC_PC_17210), and in part by the UK Medical Research Council Integrative Epidemiology Unit at the University of Bristol (Grant ref: MC_UU_12013/1). O.S.P.D. and C.M.A.H. .are funded by the Alan Turing Institute under the EPSRC grant EP/N510129/1. C.M.A.H. is supported by a Philip Leverhulme Prize.
Publisher Copyright:
© 2023 The Author(s). Published by Oxford University Press on behalf of the International Epidemiological Association.
Keywords
- Social media
- epidemiology
- cohort studies
- longitudinal studies
- data science
- big data
- mental health
- wellbeing
- data linkage
- ALSPAC
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