NM001007/An-Attention-based-Hybrid-Suicide-Ideation-Detection
This is an implementation of the attention-based hybrid architecture (Ghosh et al, 2023) for suicide/depressive social media notes detection.
This project helps mental health practitioners and social media content moderators quickly identify posts that may indicate suicidal ideation or depression. By analyzing social media text, it flags content that suggests a user might be at risk, providing an initial assessment to inform intervention or moderation decisions. The output is a classification of whether a post contains suicidal or depressive ideation.
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Use this if you need an automated tool to help screen large volumes of social media text for signs of suicide ideation or depression, particularly across multiple languages.
Not ideal if you require a diagnostic tool for individual clinical assessment, as this offers initial detection rather than a comprehensive diagnosis.
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MIT
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Jan 24, 2024
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