TY  - JOUR
Y1  - 2026/01//
JF  - International Journal of Computational Intelligence Systems
AV  - public
N2  - Identifying the emotional state of individuals has useful applications, particularly to reduce the risk of suicide. Users? thoughts on social media platforms can be used to find cues on the emotional state of individuals. Clinical approaches to suicide ideation detection primarily rely on evaluation by psychologists, medical experts, etc., which is time-consuming and requires medical expertise. Machine learning approaches have shown potential in automating suicide detection. In this regard, this study presents a soft voting ensemble model (SVEM) by leveraging random forest, logistic regression, and stochastic gradient descent classifiers using soft voting. In addition, for the robust training of SVEM, a hybrid feature engineering approach is proposed that combines term frequency-inverse document frequency and the bag of words. For experimental evaluation, ?Suicide Watch? and ?Depression? subreddits on the Reddit platform are used. Results indicate that the proposed SVEM model achieves an accuracy of 94%, better than existing approaches. The model also shows robust performance concerning precision, recall, and F1, each with a 0.93 score. ERT and deep learning models are also used, and performance comparison with these models indicates better performance of the SVEM model. Gated recurrent unit, long short-term memory, and recurrent neural network have an accuracy of 92% while the convolutional neural network obtains an accuracy of 91%. SVEM?s computational complexity is also low compared to deep learning models. Further, this study highlights the importance of explainability in healthcare applications such as suicidal ideation detection, where the use of LIME provides valuable insights into the contribution of different features. In addition, k-fold cross-validation further validates the performance of the proposed approach.
ID  - unic26964
A1  - KINA, Erol
A1  - Choi, Jin-Ghoo
A1  - Ishaq, Abid
A1  - Shafique, Rahman
A1  - Gracia Villar, Mónica
A1  - Silva Alvarado, Eduardo René
A1  - Diez, Isabel de la Torre
A1  - Ashraf, Imran
SN  - 1875-6883
KW  - Suicide ideation; machine learning; feature extraction; ensemble learning;
feature fusion
TI  - Suicide Ideation Detection Using Social Media Data and Ensemble Machine Learning Model
UR  - http://doi.org/10.1007/S44196-025-01123-9
ER  -