Abstract
It has been suggested that use of social media late at night could lead to worse mental health outcomes. We linked Twitter (‘X’) data to self-reported measures of mental health from the Avon Longitudinal Study of Parents and Children. We aimed to predict these measures from the average hour a participant posted Tweets. We used data from 310 adult participants and 18,288 Tweets. We found strong evidence the average hour participants posted Tweets was associated with depressive symptoms, anxiety symptoms, and mental wellbeing. Average hour explained almost 2% of the variation in mental wellbeing, comparable to reports of the impact of binge drinking and exercise. Participants who, on average, Tweeted through the night (23:00 to 05:00) showed meaningfully worse mental wellbeing than those who Tweeted during the daytime. Although the average hour that a participant posted their Tweets explained less of the variation in their depressive (0.2%) and anxiety (0.7%) symptoms, after stratification by age and sex these relationships became stronger. Our results could inform behavioural interventions for improving the safety of social media platforms.
| Original language | English |
|---|---|
| Article number | 34301 |
| Journal | Scientific Reports |
| Volume | 15 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 9 Oct 2025 |
Bibliographical note
For the purpose of open access, the author has applied a ‘Creative Commons Attribution (CC BY) public copyright licence to any Author Accepted Manuscript (AAM) version arising from this submission.Data Availability Statement
The dataset analysed in this study is not publicly available, as the informed consent obtained from ALSPAC participants does not allow data to be made freely available through any third party maintained public repository. ALSPAC data can be made available on request to the ALSPAC, with full instructions for applying for data access found here: http://www.bristol.ac.uk/alspac/researchers/access/.Acknowledgements
We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. The UK Medical Research Council and Wellcome (Grant reg: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. This publication is the work of the authors and Daniel Joinson, Nina Di Cara, Nello Cristianini, Edwin Simpson, Claire Haworth and Oliver Davis will serve as guarantors for the contents of this paper.Funding
Daniel Joinson is funded by the EPSRC (grant number EP/S023704/1). Oliver Davis and Claire Haworth are funded by the Alan Turing Institute under the EPSRC grant EP/N510129/1. Claire Haworth and Nina Di Cara are supported by a Phillip Leverhulme Prize. Claire Haworth and Oliver Davis are funded by CLOSER [https://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). This research was funded in whole or in part by EPSRC (grant number EP/S023704/1)
| Funders | Funder number |
|---|---|
| Engineering and Physical Sciences Research Council | EP/S023704/1 , EP/N510129/1 |
| Economic and Social Research Council | ES/K000357/1 |
ASJC Scopus subject areas
- General
