Deep Learning-Based Detection of Depression and Suicidal Tendencies in Social Media Data with Feature Selection DOI Creative Commons
Ismail BAYDİLİ, Burak Taşçı, Gülay TAŞCI

и другие.

Behavioral Sciences, Год журнала: 2025, Номер 15(3), С. 352 - 352

Опубликована: Март 12, 2025

Social media has become an essential platform for understanding human behavior, particularly in relation to mental health conditions such as depression and suicidal tendencies. Given the increasing reliance on digital communication, ability automatically detect individuals at risk through their social activity holds significant potential early intervention support. This study proposes a machine learning-based framework that integrates pre-trained language models advanced feature selection techniques improve detection of tendencies from data. We utilize six diverse datasets, collected platforms Twitter Reddit, ensuring broad evaluation model robustness. The proposed methodology incorporates Class-Weighted Iterative Neighborhood Component Analysis (CWINCA) Support Vector Machines (SVMs) classification. results indicate achieves high accuracy across multiple ranging 80.74% 99.96%, demonstrating its effectiveness identifying factors associated with issues. These findings highlight media-based automated methods complementary tools professionals. Future work will focus real-time capabilities multilingual adaptation enhance practical applicability approach.

Язык: Английский

Deep Learning-Based Detection of Depression and Suicidal Tendencies in Social Media Data with Feature Selection DOI Creative Commons
Ismail BAYDİLİ, Burak Taşçı, Gülay TAŞCI

и другие.

Behavioral Sciences, Год журнала: 2025, Номер 15(3), С. 352 - 352

Опубликована: Март 12, 2025

Social media has become an essential platform for understanding human behavior, particularly in relation to mental health conditions such as depression and suicidal tendencies. Given the increasing reliance on digital communication, ability automatically detect individuals at risk through their social activity holds significant potential early intervention support. This study proposes a machine learning-based framework that integrates pre-trained language models advanced feature selection techniques improve detection of tendencies from data. We utilize six diverse datasets, collected platforms Twitter Reddit, ensuring broad evaluation model robustness. The proposed methodology incorporates Class-Weighted Iterative Neighborhood Component Analysis (CWINCA) Support Vector Machines (SVMs) classification. results indicate achieves high accuracy across multiple ranging 80.74% 99.96%, demonstrating its effectiveness identifying factors associated with issues. These findings highlight media-based automated methods complementary tools professionals. Future work will focus real-time capabilities multilingual adaptation enhance practical applicability approach.

Язык: Английский

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