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.
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.
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.