Framework for linking and sharing social media data for high-resolution longitudinal measurement of mental health across CLOSER cohorts

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About the research

Interactions on social media have the potential to help researchers to understand human behaviour, including whether people develop good or poor mental health. To do the best science, it is important to know as much as possible about the people who are participating in the research. The longitudinal studies that make up the CLOSER consortium include people who have contributed their data to research since birth. By inviting participants in these studies to also allow researchers to derive information from their social media feeds, it will be possible to relate this information to gold-standard measures of behaviour and other aspects of life collected by the studies.

To work out the best way to do this, this project will engage with participants in the Avon Longitudinal Study of Parents and Children (ALSPAC) to find out what is acceptable to them in terms of collecting and using their interactions on social media. This will inform the development of software that collects and codes social media data while protecting the anonymity of participants, by scoring Tweets without making the text available to researchers.

This software will be shared with other studies in the CLOSER consortium to make it easy for them to invite participants to contribute their Twitter data in a safe and secure way. The high-resolution data collected in this way will help researchers to understand human behaviour and how a person’s mental health changes over time. Collecting these data in well-known groups of people will also give scientists the information they need to improve the quality of other research using social media.

Dr Claire Haworth

Dr Oliver Davis

Research leads

Dr Oliver Davis and Dr Claire Haworth (University of Bristol)

Studies used

Research outputs

  • A published paper detailing acceptable approaches to sharing social media data in longitudinal cohort studies, contributing to Understanding Patient Data.
  • Open-source software for secure linkage of Twitter data.
  • Open-source software for secure archiving and sharing of information derived from Twitter data.
  • Dataset of information derived from Twitter data in ALSPAC.
  • A knowledge exchange workshop.

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Read about other CLOSER data linkage projects.