Frontiers in artificial intelligence
Explainable detection of depression in Arabic tweets: identifying clinically meaningful linguistic markers using SHAP
By Salehi E, Raissi M, Rasouli A and 1 others.
Abstract
Background
Depression detection models for Arabic social-media text often achieve high predictive accuracy but operate as black boxes, limiting their acceptability in clinical and public-health contexts. This study aimed to develop and evaluate an explainable machine learning framework capable of identifying clinically meaningful linguistic markers of depression in Arabic tweets.
Methods
We used the ArabMindGuard dataset (3,079 manually annotated tweets; 21.4% Depressed). Tweets were processed through a standard Arabic NLP pipeline and represented using TF-IDF vectorization (unigrams and bigrams). We compared six classification pipelines (Logistic Regression, Random Forest with three imbalance-handling strategies, SVM, and XGBoost) using 5-fold stratified cross-validation with TF-IDF and resampling strictly inside each fold. The primary model, Random Forest with SMOTE, was selected for its compatibility with exact TreeSHAP computations. To assess generalizability, we externally validated the model on the independent Nassar corpus (N = 10,000). TreeSHAP was applied for interpretability, and a systematic error analysis was conducted on all false negatives and false positives.