Expression of emotion is an indicator that can contribute to the detection of mental health-related disorders. Suicide causes death to many people around the globe, and despite the suicide prevention strategies that have been employed over the years, only a few studies have explored the role of emotions in predicting suicidal behavior on social media platforms. This study explored the role of emotions from Twitter messages in detecting suicide-related content. We extracted and analyzed the characteristics of Twitter users' sentiment and behavior response (anger, fear, sadness, joy, positive, and negative) using NRC Affect Intensity Lexicon and SentiStrength techniques. A semi-supervised learning method was applied using the YATSI classifier or “Yet Another Two-Stage Idea” to efficiently recognize suicide-related tweets. The results showed that tweets associated with suicide content were exclusively related to fear, sadness, and negative sentiments. The classification results showed the potential of emotions in facilitating the detection of suicide-related content online. Our findings offer valuable insights into ongoing research on the prevention of suicide risk and other mental-related disorders on Twitter. The proposed mechanism can contribute to the development of clinical decision support systems that deal with evidence-based guidelines and generate customized recommendations.
|Journal||Biomedical Signal Processing and Control|
|Early online date||30 Dec 2020|
|Publication status||Published - Mar 2021|
Bibliographical noteCopyright 2021 Elsevier B.V., All rights reserved.
FunderThis work was funded by the Researchers Supporting Project ( RSP2020/157 ), King Saud University, Riyadh, Saudi Arabia .
- Incidents detection
- Lexicon-based approach
- Mental health
- Semi-supervised learning
- Suicidal thoughts
ASJC Scopus subject areas
- Signal Processing
- Health Informatics