Leveraging Reddit for Suicidal Ideation Detection: A Review of Machine Learning and Natural Language Processing...

Area of Science:

• Computational Social Science
• Public Health Informatics
• Artificial Intelligence in Mental Health

Background:

• Suicide is a significant global public health issue requiring effective prevention strategies.
• Traditional suicide risk screening methods face challenges in early detection.
• Individuals express suicidal thoughts on social media platforms like Reddit, offering new avenues for detection.

Purpose of the Study:

• To investigate machine learning (ML) and natural language processing (NLP) methods for detecting suicidal ideation on Reddit.
• To systematically review recent literature on ML/NLP applications for suicidality detection in online forums.

Main Methods:

• Conducted a systematic literature review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
• Selected 26 relevant studies published between 2018 and 2022 focusing on ML/NLP techniques applied to Reddit data.
• Analyzed methods for data collection, annotation, preprocessing, feature engineering, model development, and evaluation.

Main Results:

• Identified prevalent ML and NLP techniques used for detecting suicidal ideation on Reddit.
• Cataloged various Reddit-based datasets employed in developing suicidality detection models.
• Outlined common approaches across the lifecycle of model development, from data handling to evaluation.

Conclusions:

• ML and NLP offer promising tools for identifying suicidal ideation within online communities like Reddit.
• The review provides insights into current methodologies and available datasets for this critical research area.
• Further research is needed to address existing limitations and explore future directions in online suicidality detection.