Detecting Suicidal Ideation in Text with Interpretable Deep Learning: A CNN-BiGRU with Attention Mechanism

TL;DR

This paper presents a hybrid CNN-BiGRU deep learning model with attention and SHAP interpretability for detecting suicidal ideation in social media text, achieving high accuracy and outperforming existing methods.

Contribution

It introduces a novel hybrid CNN-BiGRU architecture combined with explainable AI techniques for more accurate and interpretable suicide ideation detection from social media data.

Findings

Achieved 93.97% accuracy in detecting suicidal ideation.

Outperformed state-of-the-art machine learning and deep learning models.

Validated the model's reliability with SHAP interpretability.

Abstract

Worldwide, suicide is the second leading cause of death for adolescents with past suicide attempts to be an important predictor for increased future suicides. While some people with suicidal thoughts may try to suppress them, many signal their intentions in social media platforms. To address these issues, we propose a new type of hybrid deep learning scheme, i.e., the combination of a CNN architecture and a BiGRU technique, which can accurately identify the patterns of suicidal ideation from SN datasets. Also, we apply Explainable AI methods using SHapley Additive exPlanations to interpret the prediction results and verifying the model reliability. This integration of CNN local feature extraction, BiGRU bidirectional sequence modeling, attention mechanisms, and SHAP interpretability provides a comprehensive framework for suicide detection. Training and evaluation of the system were performed on a publicly available dataset. Several performance metrics were used for evaluating model performance. Our method was found to have achieved 93.97 accuracy in experimental results. Comparative study to different state-of-the-art Machine Learning and DL models and existing literature demonstrates the superiority of our proposed technique over all the competing methods.