Beyond the Feature Level: A Cluster Analysis of Feature-Level Social Media Behaviour Patterns, Maladaptive Use, and Psychological Well-Being
Bibliographic record
Abstract
Maladaptive use of smartphones and social media is a growing issue that has received considerable attention from researchers both to map out its psychological and behavioural processes, and to develop interventions to help users regulate their use at either the device or app level. However, heterogeneous findings about the relationships between maladaptive use, mental health conditions, well-being, and patterns of smartphone use suggest the need for more nuanced examinations of feature-level usage patterns. In this study, we administered psychological self-report scales to 108 Instagram users, and tracked their use of the app for two weeks at the feature level. We found that participants overwhelmingly made use of content-consumption features and our cluster analysis revealed the existence of previously unidentified latent user groups with unique psychological and feature-use characteristics, calling into question the assumption that decreased use duration is necessarily associated with reduced maladaptive outcomes. Taken together, these findings suggest the need for a user profile-aware approach to studying maladaptive smartphone and social media use, as well as the need to investigate interventions that target outcomes beyond use-limiting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".