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Idée - Depression Detection in Social Media
MOGAM: Multimodal Object-Oriented Graph Attention Model for Depression Detection
Early detection of depression through a Multimodal Object-Oriented Graph Attention Model (MOGAM) offers scalable and versatile solutions for mental health monitoring on social media.
Automatic Screening of Depression Symptoms in Romanized Sinhala Tweets
A machine learning-based framework for the automatic screening of depression symptoms by analyzing language patterns, sentiment, and behavioral cues within a comprehensive dataset of Romanized Sinhala social media posts.
Natural Language Processing for Modeling Depression in Social Media: A Post-COVID-19 Perspective
The COVID-19 pandemic has significantly impacted research on depression modeling using natural language processing (NLP) techniques applied to social media data, leading to new datasets and a focus on the pandemic's effects on mental health.
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