Abdulrazak Yahya, Saleh and Fadzlyn Nasrini, Mostapa (2023) Suicide and self-harm prediction based on social media data using machine learning algorithms. Science in Information Technology Letters, 4 (1). pp. 12-21. ISSN 2722-4139
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Abstract
Online social networking (SN) data is a context and time rich data stream that has showed potential for predicting suicidal ideation and behaviour. Despite the obvious benefits of this digital media, predictive modelling of acute suicidal ideation (SI) remains underdeveloped at now. In combined with robust machine learning algorithms, social networking data may provide a potential path ahead. Researchers applied a machine learning models to a previously published Instagram dataset of youths. Using predictors that reflect language use and activity inside this social networking, researchers compared the performance of the out-of-sample, cross-validated model to that of earlier efforts and used a model explanation to further investigate relative predictor relevance and subject-level phenomenology. The application of ensemble learning approaches to SN data for the prediction of acute SI may reduce the complications and modelling issues associated with acute SI at these time scales. Future research is required on bigger, more diversified populations to refine digital biomarkers and assess their external validity with more rigor.
Item Type: | Article |
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Uncontrolled Keywords: | Social networking; machine learning; algorithms; suicide; self-harm. |
Subjects: | Q Science > Q Science (General) |
Divisions: | Academic Faculties, Institutes and Centres > Faculty of Cognitive Sciences and Human Development Faculties, Institutes, Centres > Faculty of Cognitive Sciences and Human Development Academic Faculties, Institutes and Centres > Faculty of Cognitive Sciences and Human Development |
Depositing User: | Saleh Al-Hababi |
Date Deposited: | 23 Oct 2023 01:22 |
Last Modified: | 23 Oct 2023 01:22 |
URI: | http://ir.unimas.my/id/eprint/43190 |
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