Much work in HCI has investigated strategies for supporting autonomous self-regulation in social media use (SMU): helping users to control their time online and ensure it serves personally valued outcomes. However, results suggest that the effectiveness and acceptability of these strategies may vary based on individual needs. Recent work has attributed this variation to motivational factors, though we currently lack data to understand how these factors influence self-regulation, user experience and well-being. We draw on Self-Determination Theory to analyse autonomous and non-autonomous patterns of motivation in 521 users of social media. Using latent profile analysis, we identify 4 ``motivational profiles'' associated with significant differences in need satisfaction, affect, and compulsive engagement. Our results clarify distinct aspects of autonomy in SMU and identify opportunities to target and personalise design interventions; they suggest autonomous regulation can be associated with better experience and well-being, though not necessarily less time online.
https://dl.acm.org/doi/10.1145/3706598.3713094
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