Abstract
It has been suggested that active use of social media (i.e. direct interactions with other users) is associated with improved mental health. Active use could help build relationships, and lead to feelings of social and emotional support. However, evidence is inconsistent, potentially because active use comprises many different behaviours. The impact of active use may depend on whether individuals are targeting specific users and the feedback they receive from others. Our study explored the relationship between the public active use Twitter users performed and received, and mental health outcomes. We linked Twitter data from 2020 to 2022 to self-reported measures of mental health from the Avon Longitudinal Study of Parents and Children. We generated variables capturing the type of active use each participant performed, and the feedback they received from other users. We produced models predicting mental health from these variables, using data from 310 adults. We found little to no evidence that engaging in more targeted interactions or receiving greater amounts of feedback on Twitter was linked to better mental health outcomes. Likewise, there was limited evidence that the amount of feedback received influenced the relationship between targeted interactions and mental health. We also found no evidence that posting more nontargeted Tweets (i.e. those not directed at any specific user) was related to mental health. However, this relationship was influenced by the amount of feedback received. As feedback increased, the relationship between nontargeted Tweets and mental wellbeing became more negative. Given that Twitter is primarily designed for information sharing purposes, social interactions on this platform may often result in weak relationships that do not provide meaningful social or emotional benefit.
| Original language | English |
|---|---|
| Article number | 101197 |
| Number of pages | 10 |
| Journal | Computers in Human Behavior Reports |
| Volume | 23 |
| Early online date | 10 Jul 2026 |
| DOIs | |
| Publication status | Published - 31 Aug 2026 |
Data Availability Statement
The data used in this study are available from ALSPAC, but restrictions apply to its availability. In order to access ALSPAC research data a formal procedure must be followed, which includes submitting a detailed study proposal and undergoing relevant training. More information regarding this process is found here: http://www.bristol.ac.uk//researchers/access/ALSPAC. ALSPAC data is not publicly available in order to protect the anonymity of participants, and prevent public access to sensitive data. The code used to generate the variables and models described throughout the methodology is available upon request.Funding
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. 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 were supported by a Phillip Leverhulme Prize. Claire Haworth and Oliver Davis were 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 was extended to March 2021 by the ESRC (grant reference: ES/K000357/1).
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- ALSPAC
- Anxiety
- Depression
- Mental health
- Social media
- Wellbeing
ASJC Scopus subject areas
- Neuroscience (miscellaneous)
- Applied Psychology
- Human-Computer Interaction
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence
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