What question did this study set out to answer?

The central aim is to optimize the detection of depression using social media data through advanced machine learning techniques.

February 28, 2026Open Access

A Multi-Platform Approach to Optimizing Depression Detection in Social Media Through Feature Engineering and Contextual Analysis

Puntos clave

The central aim is to optimize the detection of depression using social media data through advanced machine learning techniques.
Applied feature engineering and contextual analysis to social media textual content.
Utilized traditional and deep learning models for automated detection.
Conducted text pre-processing to enhance data quality and reliability.
Compared various model performances using standard evaluation metrics.
Achieved a final accuracy of 94.74% with consistent precision, recall, and F1-score.
GRU-Attention model outperformed most non-transformer-based models.
Demonstrated effectiveness in identifying linguistic patterns associated with depression.

Resumen

Depression is a prevalent mental disorder that significantly affects the well-being and normal functioning of individuals. The widespread use of social media platforms such as Twitter (then X), Reddit, and Facebook provides an unprecedented opportunity to study user-generated textual content in the context of initial identification of depressive symptomatology. This paper proposes a holistic, data-driven approach to automated depression detection that combines traditional machine learning and modern deep learning techniques. The multi-platform data were subjected to stringent pre-processing, including text normalization, tokenisation, lemmatisation, and random oversampling to correct noise, heterogeneity, and class imbalance, ensuring healthy and reliable model training. The proposed models were compared systematically to a set of other traditional machine learning, deep learning, and attention-based baselines in the same setting of experiments, with the assessment of performance checked with standard evaluation metrics and visual analysis to provide an unbiased and strong performance measurement. The GRU-Attention model was shown to exhibit strong capability for long-range contextual interactions and emotionally significant tokens, achieving higher results than most non-transformer-based models. To boost predictive stability, generalization, and robustness, the results of the most successful models were stacked using a simple averaging method, yielding a final accuracy of 94.74%, with equal precision, recall, and F1-score. Such a wide-scale analysis, including confusion matrices and AUC-ROC curves, validated the effectiveness of the framework in minimizing misclassifications and identifying subtle linguistic patterns. The proposed system is a scalable, interpretable, and non-invasive system to detect depression at an early stage, which has a vast potential for being applied in digital mental health monitoring and intervention approaches.

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