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primary topic: Suicide Ideation Detection Using Social Media Data and Ensemble Machine Learning Model

Suicide Ideation Detection Using Social Media Data and Ensemble Machine Learning Model
type: AcademicArticle
type: Article
abstract: 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.
date: 2026-01
is part of: International Journal of Computational Intelligence Systems
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
has document: Suicide Ideation Detection Using Social Media Data and Ensemble Machine Learning Model (Texto)
has document: Suicide Ideation Detection Using Social Media Data and Ensemble Machine Learning Model (Otro)
subject: Engineering

Suicide Ideation Detection Using Social Media Data and Ensemble Machine Learning Model (Texto)
type: Document
has file: s44196-025-01123-9_reference.pdf
has part: s44196-025-01123-9_reference.pdf

International Journal of Computational Intelligence Systems
type: Collection
issn: 18756883

Erol KINA
type: Person
given name: Erol
family name: KINA

Isabel de la Torre Diez
type: Person
given name: Isabel de la Torre
family name: Diez

Imran Ashraf
type: Person
given name: Imran
family name: Ashraf

Mónica Gracia Villar
type: Person
given name: Mónica
family name: Gracia Villar

Abid Ishaq
type: Person
given name: Abid
family name: Ishaq

Jin-Ghoo Choi
type: Person
given name: Jin-Ghoo
family name: Choi

Rahman Shafique
type: Person
given name: Rahman
family name: Shafique

Eduardo René Silva Alvarado
type: Person
given name: Eduardo René
family name: Silva Alvarado

Suicide Ideation Detection Using Social Media Data and Ensemble Machine Learning Model (Otro)
type: Document
is version of: Suicide Ideation Detection Using Social Media Data and Ensemble Machine Learning Model (Texto)
is volatile version of: Suicide Ideation Detection Using Social Media Data and Ensemble Machine Learning Model (Texto)
is index codes version of: Suicide Ideation Detection Using Social Media Data and Ensemble Machine Learning Model (Texto)
has file: indexcodes.txt
has part: indexcodes.txt

HTML Summary of #26964 Suicide Ideation Detection Using Social Media Data and Ensemble Machine Learning Model
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primary topic: Suicide Ideation Detection Using Social Media Data and Ensemble Machine Learning Model

Engineering
type: Concept
preferred label: Ingeniería @es