CautionSuicide: A Deep Learning Based Approach for Detecting Suicidal Ideation in Real Time Chatbot Conversation
Nelly Elsayed, Zag ElSayed, Murat Ozer

TL;DR
This paper presents a deep learning model designed to detect suicidal ideation in real-time chatbot conversations, aiming to enable early intervention and support for at-risk individuals through digital platforms.
Contribution
It introduces a novel, simple deep learning approach for identifying suicidal thoughts in digital content, specifically focusing on chatbot interactions, and proposes an integrated support framework.
Findings
Effective detection of suicidal ideation in chatbot conversations
Framework for integrating detection with support systems
Potential for real-time intervention in digital communication
Abstract
Suicide is recognized as one of the most serious concerns in the modern society. Suicide causes tragedy that affects countries, communities, and families. There are many factors that lead to suicidal ideations. Early detection of suicidal ideations can help to prevent suicide occurrence by providing the victim with the required professional support, especially when the victim does not recognize the danger of having suicidal ideations. As technology usage has increased, people share and express their ideations digitally via social media, chatbots, and other digital platforms. In this paper, we proposed a novel, simple deep learning-based model to detect suicidal ideations in digital content, mainly focusing on chatbots as the primary data source. In addition, we provide a framework that employs the proposed suicide detection integration with a chatbot-based support system.
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Taxonomy
TopicsMental Health via Writing · Suicide and Self-Harm Studies · Digital Mental Health Interventions
