Analysing Social Media Addiction and Deceptive Psychological Behaviour Among Students Using a Hybrid Deep Ensemble Framework
Bibliographic record
Abstract
The growing incidence of social media addiction and manipulative psychological behaviours among students have caused worries over academic decline and mental health challenges. Conventional detection approaches encounter difficulties with contextual behavioural changes, requiring a mixed deep learning strategy. This study presents a Hybrid Deep Ensemble Framework (HDEF-SMBPA), which combines XGBoost for behavioural features with CNN for text-based sentiment and deception analysis. The system utilises SHAP-based explainable AI (XAI) to improve interpretability. Experimental findings indicate an accuracy of 94.2%, a recall of 93.5%, and a ROC-AUC of 0.97, exceeding those of conventional models. The findings underscore adverse academic associations, offering insights for psychologists and educators. The proposed system surpasses current approaches, providing a scalable, real-time solution for detecting and mitigating digital behavioural threats.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".