Explainable AI-based suicidal and non-suicidal ideations detection from social media text with enhanced ensemble technique
Abstract This research presents a novel framework for distinguishing between actual and non-suicidal ideation in social media interactions using an ensemble technique. The prompt identification of sentiments on social networking platforms is crucial for timely intervention serving as a key tactic in...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-84275-6 |
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| author | Daniyal Alghazzawi Hayat Ullah Naila Tabassum Sahar K. Badri Muhammad Zubair Asghar |
| author_facet | Daniyal Alghazzawi Hayat Ullah Naila Tabassum Sahar K. Badri Muhammad Zubair Asghar |
| author_sort | Daniyal Alghazzawi |
| collection | DOAJ |
| description | Abstract This research presents a novel framework for distinguishing between actual and non-suicidal ideation in social media interactions using an ensemble technique. The prompt identification of sentiments on social networking platforms is crucial for timely intervention serving as a key tactic in suicide prevention efforts. However, conventional AI models often mask their decision-making processes primarily designed for classification purposes. Our methodology, along with an updated ensemble method, bridges the gap between Explainable AI and leverages a variety of machine learning algorithms to improve predictive accuracy. By leveraging Explainable AI’s interpretability to analyze the features, the model elucidates the reasoning behind its classifications leading to a comprehension of hidden patterns associated with suicidal ideations. Our system is compared to cutting-edge methods on several social media datasets using experimental evaluations, demonstrating that it is superior, since it detects suicidal content more accurately than others. Consequently, this study presents a more reliable and interpretable strategy (F1-score for suicidal = 95.5% and Non-Suicidal = 99%), for monitoring and intervening in suicide-related online discussions. |
| format | Article |
| id | doaj-art-06769329a72e4ee683ac07663fb54c72 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-06769329a72e4ee683ac07663fb54c722025-01-12T12:18:47ZengNature PortfolioScientific Reports2045-23222025-01-0115111910.1038/s41598-024-84275-6Explainable AI-based suicidal and non-suicidal ideations detection from social media text with enhanced ensemble techniqueDaniyal Alghazzawi0Hayat Ullah1Naila Tabassum2Sahar K. Badri3Muhammad Zubair Asghar4Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz UniversityGomal Research Institute of Computing (GRIC), Faculty of Computing, Gomal UniversityGomal Research Institute of Computing (GRIC), Faculty of Computing, Gomal UniversityInformation Systems Department, Faculty of Computing and Information Technology, King Abdulaziz UniversityGomal Research Institute of Computing (GRIC), Faculty of Computing, Gomal UniversityAbstract This research presents a novel framework for distinguishing between actual and non-suicidal ideation in social media interactions using an ensemble technique. The prompt identification of sentiments on social networking platforms is crucial for timely intervention serving as a key tactic in suicide prevention efforts. However, conventional AI models often mask their decision-making processes primarily designed for classification purposes. Our methodology, along with an updated ensemble method, bridges the gap between Explainable AI and leverages a variety of machine learning algorithms to improve predictive accuracy. By leveraging Explainable AI’s interpretability to analyze the features, the model elucidates the reasoning behind its classifications leading to a comprehension of hidden patterns associated with suicidal ideations. Our system is compared to cutting-edge methods on several social media datasets using experimental evaluations, demonstrating that it is superior, since it detects suicidal content more accurately than others. Consequently, this study presents a more reliable and interpretable strategy (F1-score for suicidal = 95.5% and Non-Suicidal = 99%), for monitoring and intervening in suicide-related online discussions.https://doi.org/10.1038/s41598-024-84275-6Machine learningSuicidal ideationSocial media textEnsemble modelsExplainable AI |
| spellingShingle | Daniyal Alghazzawi Hayat Ullah Naila Tabassum Sahar K. Badri Muhammad Zubair Asghar Explainable AI-based suicidal and non-suicidal ideations detection from social media text with enhanced ensemble technique Scientific Reports Machine learning Suicidal ideation Social media text Ensemble models Explainable AI |
| title | Explainable AI-based suicidal and non-suicidal ideations detection from social media text with enhanced ensemble technique |
| title_full | Explainable AI-based suicidal and non-suicidal ideations detection from social media text with enhanced ensemble technique |
| title_fullStr | Explainable AI-based suicidal and non-suicidal ideations detection from social media text with enhanced ensemble technique |
| title_full_unstemmed | Explainable AI-based suicidal and non-suicidal ideations detection from social media text with enhanced ensemble technique |
| title_short | Explainable AI-based suicidal and non-suicidal ideations detection from social media text with enhanced ensemble technique |
| title_sort | explainable ai based suicidal and non suicidal ideations detection from social media text with enhanced ensemble technique |
| topic | Machine learning Suicidal ideation Social media text Ensemble models Explainable AI |
| url | https://doi.org/10.1038/s41598-024-84275-6 |
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