Automated Detection of Depression Using Social Media Data

Introduction

The rise of social media platforms has provided a new avenue for understanding mental health issues such as depression. This project proposal focuses on developing a system to automatically detect signs of depression in social media posts using machine learning techniques. The system aims to analyze textual data from platforms like Twitter and Reddit to identify users who may be experiencing depression.

Background

Research indicates that social media data can be a valuable resource for identifying mental health issues. Various studies have utilized machine learning models to analyze linguistic and behavioral patterns indicative of depression. Techniques such as sentiment analysis and natural language processing (NLP) are commonly employed to extract meaningful insights from social media text.

Project Objective

The primary objective of this project is to create an automated system capable of detecting depression in social media posts. The system will leverage machine learning models to analyze text data and classify users into categories such as "not depressed," "moderately depressed," and "severely depressed."

Methodology

1. Data Collection and Preprocessing

• Datasets: Use datasets such as the Reddit Depression Corpora and other publicly available datasets from platforms like Twitter.
• Preprocessing: Clean and preprocess the text data by removing noise, normalizing text, and extracting relevant features.

2. Model Development

• Feature Extraction: Utilize NLP techniques to extract features such as word embeddings, sentiment scores, and linguistic markers.
• Machine Learning Models: Implement models like RoBERTa for text classification, which have shown effectiveness in previous research[2].

3. Training and Evaluation

• Training: Train the model using labeled datasets, optimizing for accuracy and precision.
• Evaluation Metrics: Evaluate the model's performance using metrics such as accuracy, precision, recall, and F1-score.

Expected Outcomes

The proposed system is expected to accurately identify signs of depression in social media posts. By leveraging advanced machine learning techniques, the system should provide reliable classifications that can aid in early intervention and support for individuals experiencing depression.

Conclusion

This project aims to enhance the detection of depression through social media analysis by developing a robust machine learning-based system. The integration of NLP techniques with powerful classification models is anticipated to improve the accuracy and reliability of depression detection.

For further details on related research, please refer to the paper "Automated Detection of Depression Using Social Media Data," available at https://ieeexplore.ieee.org/document/8768790.

The dataset used for this project can be accessed at https://github.com/rafalposwiata/depression-detection-lt-edi-2022.