Explainable Depression Detection on Reddit: A BiLSTM-Attention Framework with SHAP and LIME Interpretability

Abstract

Early identification of depression by social media content analysis has drawn increasing attention as it is a common mental health issue. To identify sadness in Reddit posts, this study proposes a novel framework that combines advanced deep learning and explainable AI techniques. A hybrid CNN+XGBoost model was implemented as the baseline, achieving 92.68% accuracy. To address its limitations, a BiLSTM with an Attention mechanism was developed, which captured long-term sequential dependencies and emphasized clinically significant tokens. The proposed model significantly outperformed the baseline, achieving 97.46% accuracy, a 0.9746 F1-score, and balanced precision–recall performance. For transparency, SHAP and LIME were applied to highlight influential linguistic cues at both local and global levels, thereby improving interpretability. The findings demonstrate the dual strength of predictive performance and explainability, offering a reliable framework for potential clinical and real-world applications.

Introduction

• Depression affects ~280 million people globally and contributes to ~700,000–800,000 suicides annually (WHO).

• Social media platforms (Reddit, Twitter, Facebook) are rich with emotional expressions (text, emojis, images) that can serve as digital markers of mental health.

• Early detection of depressive tendencies via social media can enable timely interventions, especially when clinical resources are inaccessible.

2. Problem & Motivation

• Traditional diagnosis methods (clinical interviews, questionnaires) are time-consuming and costly.

• Social media offers an alternative, but challenges include:

  • Unstructured, noisy data

  • Small, imbalanced datasets

  • Limited model interpretability

• Most existing research focuses on classical ML and DL methods, with limited application of explainable AI (XAI).

3. Objective of the Study

• Develop an interpretable deep learning model to detect depression from Reddit posts.

• Implement and compare:

  • Baseline Model: CNN + XGBoost

  • Proposed Model: BiLSTM + Attention + Explainability (SHAP & LIME)

• Balance predictive performance with transparency, aiming for clinically usable results.

4. Literature Review Highlights

???? ML Approaches

• Traditional models (SVM, Random Forest, Naïve Bayes) use handcrafted features.

• Word embeddings like Word2Vec improved results, but models lack semantic depth.

???? Deep Learning (DL)

• CNNs, LSTMs, BiLSTMs, and Transformers outperform ML by capturing context.

• BiLSTM + Attention improves on Reddit and culturally specific texts but suffers from interpretability issues.

???? Explainable AI (XAI)

• SHAP, LIME, and attention layers enhance model transparency.

• Still underused in mental health detection, especially with multilingual and real-world deployment concerns.

???? Gaps Identified

• Lack of:

  • Cross-platform datasets

  • Multilingual/culturally diverse models

  • Reproducibility and clinical readiness

  • Token-level interpretability

5. Methodology

???? Dataset

• Curated ~20,000 Reddit posts (labeled depression vs. non-depression).

• Preprocessing: Tokenization, stop-word removal, padding, label encoding, and Word2Vec embeddings (300 dimensions).

?? Model Architectures

A. CNN + XGBoost (Baseline)

• CNN extracts local features (e.g., “can’t sleep”).

• XGBoost boosts classification using tree ensembles.

• Handles imbalance and noisy data well.

B. BiLSTM + Attention (Proposed)

• Captures long-term and contextual dependencies across post sequences.

• Attention highlights meaningful tokens, improving interpretability and accuracy.

• SHAP & LIME used to explain both global feature importance and local predictions.

6. Evaluation Metrics

• Confusion Matrix used to compute:

  • Accuracy: (TP + TN) / Total

  • Precision: TP / (TP + FP)

  • Recall (Sensitivity): TP / (TP + FN)

  • F1-Score: Harmonic mean of Precision and Recall

• AUC provides additional performance insight for imbalanced classes.

7. Explainability Tools

• SHAP: Uses game theory to attribute predictions to features globally and locally.

• LIME: Builds local interpretable models for each prediction instance.

• Together, they offer a complementary understanding of the model’s decision-making process.

8. Results & Findings

???? Baseline Model (CNN + XGBoost)

• Performed reasonably well with structured feature extraction and ensemble classification.

• Confusion matrix and classification metrics (F1, precision, recall) indicated solid baseline performance.

???? Proposed Model (BiLSTM + Attention + SHAP/LIME)

• Showed superior performance across all metrics.

• Attention mechanism successfully emphasized key depression-related tokens.

• SHAP & LIME enhanced trust and understanding of decisions—crucial for clinical applications.

Conclusion

This study presented a thorough structure for depression detection from Reddit posts, combining deep learning with explainability. The baseline CNN + XGBoost model established a strong foundation, but the proposed BiLSTM with the Attention mechanism achieved superior performance by effectively capturing sequential dependencies and focusing on critical linguistic cues. The significant improvement in accuracy and F1-score confirms the robustness of the proposed method. Furthermore, SHAP and LIME explanations provided insightful information about how the model makes decisions ensuring both transparency and trust. These findings demonstrate the importance of combining predictive accuracy with interpretability for applications in mental health. In the future, the framework can be extended to multi-class datasets, larger multilingual corpora, and models based on transformers to improve generalizability. Actual implementation in clinical or social media monitoring contexts could further validate its practical utility and ethical application.

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Copyright © 2025 Ranjeet Singh Thakur, JP Singh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.