A psycho-ecological signal recognition framework for user behavior prediction on digital media platforms
Bibliographic record
Abstract
BACKGROUND: Digital media usage has become an integral part of daily life, but prolonged or emotionally driven engagement-especially during late-night hours-may lead to concerns about behavioral and mental health. Existing predictive systems fail to account for the nuanced interplay between users' internal psychological states and their surrounding ecological contexts. OBJECTIVE: This study aims to develop a psychologically and ecologically informed behavior prediction model to identify high-risk patterns of digital media usage and support early-stage intervention strategies. METHODS: We propose a Dual-Channel Cross-Attention Network (DCCAN) architecture composed of three layers: signal identification (for psychological and ecological encoding), interaction modeling (via cross-modal attention), and behavior prediction. The model was trained and tested on a dataset of 9,782 users and 51,264 behavior sequences, annotated with labels for immersive usage, late-night activity, and susceptibility to health misinformation. RESULTS: The DCCAN model achieved superior performance across all three tasks, especially in immersive usage prediction (F1-score: 0.891, AUC: 0.913), outperforming LSTM, GRU, and XGBoost baselines. Ablation studies confirmed the critical role of both psychological and ecological signals, as well as the effectiveness of the cross-attention mechanism. CONCLUSIONS: Incorporating psychological and ecological modalities through attention-based fusion yields interpretable and accurate predictions for digital risk behaviors. This framework shows promise for scalable, real-time behavioral health monitoring and adaptive content moderation on media platforms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".