Detecting suicidality on social media: Machine learning at rescue
The rise in technological advancements and Social Networking Sites (SNS) made people more engaged in their virtual lives. Research has revealed that people feel more comfortable posting their feelings, including suicidal thoughts, on SNS than discussing them through face-to-face settings due to the...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-07-01
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| Series: | Egyptian Informatics Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110866523000233 |
| _version_ | 1797812085213626368 |
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| author | Syed Tanzeel Rabani Akib Mohi Ud Din Khanday Qamar Rayees Khan Umar Ayoub Hajam Ali Shariq Imran Zenun Kastrati |
| author_facet | Syed Tanzeel Rabani Akib Mohi Ud Din Khanday Qamar Rayees Khan Umar Ayoub Hajam Ali Shariq Imran Zenun Kastrati |
| author_sort | Syed Tanzeel Rabani |
| collection | DOAJ |
| description | The rise in technological advancements and Social Networking Sites (SNS) made people more engaged in their virtual lives. Research has revealed that people feel more comfortable posting their feelings, including suicidal thoughts, on SNS than discussing them through face-to-face settings due to the social stigma associated with mental health. This research study aims to develop a multi-class machine learning classifier for identifying suicidal risk levels in social media posts. The proposed Enhanced Feature Engineering Approach for Suicidal Risk Identification (EFASRI) is used to extract features from a novel dataset collected from Twitter and Reddit platforms. Three machine learning algorithms, i.e. Support Vector Machine (SVM), Random Forest (RF) and Extreme Gradient Boosting (XGB) were employed for classification. The study demonstrates significant improvements in the precision, recall, and overall accuracy compared to previous research that used classical feature extraction mechanisms. The best-performing algorithm, Extreme Gradient Boosting (XGB), achieved an overall accuracy of 96.33%. The findings imply that different features contain different levels of information, and the right combination of the features supplied to the machine learning algorithms may improve the prediction results. |
| first_indexed | 2024-03-13T07:33:17Z |
| format | Article |
| id | doaj.art-4ae2d86b46a946d0b5a5d64c0d38b0ab |
| institution | Directory Open Access Journal |
| issn | 1110-8665 |
| language | English |
| last_indexed | 2024-03-13T07:33:17Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Informatics Journal |
| spelling | doaj.art-4ae2d86b46a946d0b5a5d64c0d38b0ab2023-06-04T04:23:20ZengElsevierEgyptian Informatics Journal1110-86652023-07-01242291302Detecting suicidality on social media: Machine learning at rescueSyed Tanzeel Rabani0Akib Mohi Ud Din Khanday1Qamar Rayees Khan2Umar Ayoub Hajam3Ali Shariq Imran4Zenun Kastrati5Department of Computer Science, BGSBU, Rajouri, IndiaDepartment of Computer Science, BGSBU, Rajouri, IndiaDepartment of Computer Science, BGSBU, Rajouri, IndiaDepartment of Computer Science, BGSBU, Rajouri, IndiaDepartment of Computer Science (IDI), Norwegian University of Science and Technology (NTNU), Norway; Corresponding author.Department of Informatics, Linnaeus University (LNU), SwedenThe rise in technological advancements and Social Networking Sites (SNS) made people more engaged in their virtual lives. Research has revealed that people feel more comfortable posting their feelings, including suicidal thoughts, on SNS than discussing them through face-to-face settings due to the social stigma associated with mental health. This research study aims to develop a multi-class machine learning classifier for identifying suicidal risk levels in social media posts. The proposed Enhanced Feature Engineering Approach for Suicidal Risk Identification (EFASRI) is used to extract features from a novel dataset collected from Twitter and Reddit platforms. Three machine learning algorithms, i.e. Support Vector Machine (SVM), Random Forest (RF) and Extreme Gradient Boosting (XGB) were employed for classification. The study demonstrates significant improvements in the precision, recall, and overall accuracy compared to previous research that used classical feature extraction mechanisms. The best-performing algorithm, Extreme Gradient Boosting (XGB), achieved an overall accuracy of 96.33%. The findings imply that different features contain different levels of information, and the right combination of the features supplied to the machine learning algorithms may improve the prediction results.http://www.sciencedirect.com/science/article/pii/S1110866523000233Suicidal ideationSocial mediaFeature engineeringMachine learningEnsemble learning |
| spellingShingle | Syed Tanzeel Rabani Akib Mohi Ud Din Khanday Qamar Rayees Khan Umar Ayoub Hajam Ali Shariq Imran Zenun Kastrati Detecting suicidality on social media: Machine learning at rescue Egyptian Informatics Journal Suicidal ideation Social media Feature engineering Machine learning Ensemble learning |
| title | Detecting suicidality on social media: Machine learning at rescue |
| title_full | Detecting suicidality on social media: Machine learning at rescue |
| title_fullStr | Detecting suicidality on social media: Machine learning at rescue |
| title_full_unstemmed | Detecting suicidality on social media: Machine learning at rescue |
| title_short | Detecting suicidality on social media: Machine learning at rescue |
| title_sort | detecting suicidality on social media machine learning at rescue |
| topic | Suicidal ideation Social media Feature engineering Machine learning Ensemble learning |
| url | http://www.sciencedirect.com/science/article/pii/S1110866523000233 |
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