Suicidal Ideation Detection on Social Media: A Review of Machine Learning Methods

• URL: http://arxiv.org/abs/2201.10515v1
• Date: Tue, 25 Jan 2022 18:23:47 GMT
• Title: Suicidal Ideation Detection on Social Media: A Review of Machine Learning Methods
• Authors: Asma Abdulsalam and Areej Alhothali
• Abstract summary: Many studies have been carried out to identify suicidal ideation and behaviors in social media. This paper presents a comprehensive summary of current research efforts to detect suicidal ideation using machine learning algorithms on social media.
• Score: 0.34265828682659694
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Social media platforms have transformed traditional communication methods by allowing users worldwide to communicate instantly, openly, and frequently. People use social media to express their opinion and share their personal stories and struggles. Negative feelings that express hardship, thoughts of death, and self-harm are widespread in social media, especially among young generations. Therefore, using social media to detect and identify suicidal ideation will help provide proper intervention that will eventually dissuade others from self-harming and committing suicide and prevent the spread of suicidal ideations on social media. Many studies have been carried out to identify suicidal ideation and behaviors in social media. This paper presents a comprehensive summary of current research efforts to detect suicidal ideation using machine learning algorithms on social media. This review 24 studies investigating the feasibility of social media usage for suicidal ideation detection is intended to facilitate further research in the field and will be a beneficial resource for researchers engaged in suicidal text classification.

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