Abstract
Abstract
Background
Existing digital mental health research tends to frame social media in individualistic terms, emphasising screen-time, self-control, or personal vulnerability. Less attention has been given to the role of algorithmic recommendation systems in population mental health. We aimed to develop a grounded theory of how adolescents experience algorithmically curated feeds and how these experiences relate to mental health and wellbeing.
Methods
We conducted semi-structured, photo-elicitation interviews with 27 UK young people aged 14–19 years. Participants shared screenshots from their TikTok ‘For You’ and Instagram ‘Explore’ pages to support discussion of algorithmically recommended content. Data were analysed using constructivist grounded theory, with iterative coding, memo-writing, and theoretical sampling.
Results
We identify algorithmically structured exposure as a central mechanism through which engagement-driven recommendation systems shape young people’s digital mental health. Algorithms organised what content participants encountered, how frequently, and with what emotional intensity, within broader socioecological contexts. Three interlocking processes sustained exposure; algorithmic reinforcement of engagement, emotional feedback loops between mood and recommendations, and a persistent sense of limited control despite high awareness. Although participants developed adaptive strategies and algorithmic literacy, these were unevenly distributed and often insufficient within engagement-driven, profit-oriented systems.
Conclusions
Digital mental health may be produced through interactions between individual capacities, social environments, and commercially driven platform design. Protecting population mental health therefore requires multi-level action, including developmentally appropriate social media and algorithmic literacy education alongside structural reforms to recommendation systems, transparency, and platform governance.