When the Algorithm Becomes Comfort: Rethinking Emotional Self-Regulation in the Digital Age Beyond Screen Time: A Framework for Algorithmic Mood Regulation

Not long ago, turning to media during difficult moments meant making a conscious choice. Someone might play a favourite song after a stressful day, watch a comforting film, or call a friend for support. Today, however, emotional relief often begins with a much simpler action unlocking a phone and opening a personalised social media platform (Kross et al., 2021; Valkenburg at al., 2022). What follows is rarely planned. Instead, recommendation algorithms gradually present content that distracts, comforts, val idates, or entertains, often without the user actively deciding what they want to watch (Kozyreva et al., 2024). This paper begins with a simple observation: people are not only consuming algorithmically curated content; they are increasingly relying on it during moments of emotional discomfort (Kross et al., 2021; Gross, 2015). While existing literature has explored social media addiction, doom scrolling, digital well being, and emotion regulation, relatively little attention has been given to the psycholo gical role recommendation algorithms may play once people enter these digital environments. (Gross, 2015; Kross et al., 2021; Ryan et al., 2014; Valkenburg et al., 2022) To explore this shift, the paper proposes Algorithmic Mood Regulation (AMR) as a conce ptual framework describing the tendency to rely on personalised recommendation systems as part of everyday emotional regulation. Rather than presenting AMR as a disorder or replacing established theories, this paper argues that it offers a useful perspecti ve for understanding how digital platforms are becoming integrated into emotional coping. By connecting observations from daily life with theories of emotion regulation, media psychology, and digital behaviour, the framework invites future empirical resear ch into the growing relationship between personalised algorithms and emotional well being.