Instagram, Depression, and Dark Flow - Using Social Media as a Maladaptive Coping Mechanism
Bibliographic record
Abstract
Background: Instagram is a popular social media platform which uses algorithms to tailor content to the specific interests of individual users. We proposed that this tailored content induces a flow state where users become completely absorbed and time passes imperceptibly, creating a highly satisfying experience. For those who use Instagram to escape from depression, Instagram may provide the relief that they seek, but at a cost – neglecting real-life social supports and work/academic commitments. This neglect leads to further problems that prompt greater Instagram dependence. Methods: Using a repeated-measures design, 114 participants engaged in three conditions (Control, Generalized, and Personalized) for 10 minutes each. Participants answered questions about flow and positive affect (after each condition), as well as their Instagram use habits, depression, reasons for using Instagram, and time spent on Instagram. Results: We demonstrated that Instagram’s algorithms were effective. Participants experienced significantly greater flow and positive affect when using their tailored-content Instagram compared to a generalized account based on the interests of 30 random users. As evidence for our proposed cycle of dependence, we showed significant associations between depression, flow, and a measure of problematic Instagram use, as well as associations between being motivated to use Instagram to escape problems and problematic Instagram use. Conclusions: These results suggest that, at least for a subset of individuals with depression, Instagram serves as a maladaptive, escape-coping mechanism which induces flow and elevates mood, but ultimately leads to more problems due to overextended Instagram use.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".