Text Analysis for Depression Detection: Mental Health Digital Transformation.

Area of Science:

• Computational linguistics
• Mental health informatics
• Artificial intelligence in healthcare

Background:

• Depression is a global mental health challenge.
• Social media data offers potential for early detection.
• Existing AI models may struggle with social media language nuances.

Purpose of the Study:

• To develop an AI model for early depression identification using social media.
• To enhance AI model performance by adapting pre-trained language models to social media data.

Main Methods:

• Utilized BERT for feature extraction from user posts.
• Employed a Convolutional Neural Network (CNN) for classification.
• Introduced a novel approach of pre-training BERT on Reddit data before integration into a BERT+CNN architecture.

Main Results:

• The proposed pre-training strategy on Reddit data aims to improve BERT's understanding of social media language.
• The BERT+CNN architecture with adapted BERT is expected to enhance depression detection accuracy.

Conclusions:

• AI, particularly adapted language models, shows promise for scalable mental health monitoring.
• Leveraging social media data and advanced AI techniques can aid in early identification of mental health conditions like depression.