GPR-233 Human-Assisted AI for Detecting Mental Health Indicators in Social Media

Mental health is essential to overall well-being, and mental illness includes conditions that affect a person’s psychological health, causing significant distress and limiting daily functioning. With advancements in technology, social media has become a platform where individuals openly share their emotions and thoughts, offering a unique window into their psychological states. However, traditional machine learning models struggle to interpret social media data's wide range of linguistic nuances. To analyze this data effectively, collaboration with human experts is crucial. This study proposes an innovative human-AI teaming framework that integrates human expertise with artificial intelligence (AI) to address these challenges. Our framework leverages multi-dimensional data along with expert feedback to identify factors contributing to mental illness. Through extensive testing on Reddit data, our model demonstrates a 9% improvement in performance over the state-of-the-art model, underscoring its efficacy and impact.

Publication Details

Journal
DigitalCommons - Kennesaw State University (Kennesaw State University)
Published
2024-11-20
Primary Topic
Magnetic confinement fusion research
Type
article
Field-Weighted Citation Impact
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article

GPR-233 Human-Assisted AI for Detecting Mental Health Indicators in Social Media

DigitalCommons - Kennesaw State University (Kennesaw State University)
Magnetic confinement fusion research
article

GPR-233 Human-Assisted AI for Detecting Mental Health Indicators in Social Media

article en

Abstract

Mental health is essential to overall well-being, and mental illness includes conditions that affect a person’s psychological health, causing significant distress and limiting daily functioning. With advancements in technology, social media has become a platform where individuals openly share their emotions and thoughts, offering a unique window into their psychological states. However, traditional machine learning models struggle to interpret social media data's wide range of linguistic nuances. To analyze this data effectively, collaboration with human experts is crucial. This study proposes an innovative human-AI teaming framework that integrates human expertise with artificial intelligence (AI) to address these challenges. Our framework leverages multi-dimensional data along with expert feedback to identify factors contributing to mental illness. Through extensive testing on Reddit data, our model demonstrates a 9% improvement in performance over the state-of-the-art model, underscoring its efficacy and impact.

DigitalCommons - Kennesaw State University (Kennesaw State University)
Openalex Percentile: Top 75%
Magnetic confinement fusion research
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