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Detection of Suicidal Ideation on Social Media using Machine Learning Approaches

by Ashraful Goni, Md. Umor Faruk Jahangir, Rajarshi Roy Chowdhury, Farjana Akter, Khaled Hussain
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 51
Year of Publication: 2024
Authors: Ashraful Goni, Md. Umor Faruk Jahangir, Rajarshi Roy Chowdhury, Farjana Akter, Khaled Hussain
10.5120/ijca2024924161

Ashraful Goni, Md. Umor Faruk Jahangir, Rajarshi Roy Chowdhury, Farjana Akter, Khaled Hussain . Detection of Suicidal Ideation on Social Media using Machine Learning Approaches. International Journal of Computer Applications. 186, 51 ( Nov 2024), 8-14. DOI=10.5120/ijca2024924161

@article{ 10.5120/ijca2024924161,
author = { Ashraful Goni, Md. Umor Faruk Jahangir, Rajarshi Roy Chowdhury, Farjana Akter, Khaled Hussain },
title = { Detection of Suicidal Ideation on Social Media using Machine Learning Approaches },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2024 },
volume = { 186 },
number = { 51 },
month = { Nov },
year = { 2024 },
issn = { 0975-8887 },
pages = { 8-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number51/detection-of-suicidal-ideation-on-social-media-using-machine-learning-approaches/ },
doi = { 10.5120/ijca2024924161 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-12-01T00:09:59.618818+05:30
%A Ashraful Goni
%A Md. Umor Faruk Jahangir
%A Rajarshi Roy Chowdhury
%A Farjana Akter
%A Khaled Hussain
%T Detection of Suicidal Ideation on Social Media using Machine Learning Approaches
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 51
%P 8-14
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Early detection and prevention in suicide cases are crucial for saving lives, as timely interventions can reduce the risk of self-harm. Identifying individuals at risk before an incident occurs is a challenging task. The growing use of social media offers unique insights into individuals' behaviours like thoughts, feelings, and intentions. Therefore, this study is essential as understanding effective methods for identifying and preventing suicide can help address a major public health concern and save lives. This research addresses the use of machine learning (ML) models for identifying suicide cases and conversely, preventing them based on social media posts. In this paper, six ML classifiers, including Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), and XGBoost (XGB), are employed for the classification task using social platforms data analysis. The proposed ML model’s performances are evaluated using the publicly available datasets from the Kaggle and Reddit. Compared with all the other ML models SVM shows as the top performer with an accuracy of 93.85%, precision of 93.86%, recall of 93.85%, and an F1-score of 93.85, whilst the LR classifier achieved almost similar results. On the other hand, the DT classifier gained lowest classification performances. The study signifies that the effectiveness of the proposed ML approach in classifying nuanced mental health-related content, contributing to ongoing efforts in suicide prevention through advanced computational methods.

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Index Terms

Computer Science
Information Sciences

Keywords

Suicide Ideation Text Classification Machine Learning Social Media Platforms Suicide Prediction

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