Multi Class Depression Detection Through Tweets using Artificial Intelligence

TL;DR

This paper presents a novel approach to detect and classify five types of depression from tweets using BERT and explainable AI, achieving high accuracy and addressing previous dataset labeling issues.

Contribution

It introduces a multi-class depression detection model utilizing BERT and lexicon labeling, with explainability, for the first time on Twitter data.

Findings

BERT achieved 96% accuracy in depression classification.

Five depression types were successfully distinguished.

Explainable AI highlighted relevant tweet parts for diagnosis.

Abstract

Depression is a significant issue nowadays. As per the World Health Organization (WHO), in 2023, over 280 million individuals are grappling with depression. This is a huge number; if not taken seriously, these numbers will increase rapidly. About 4.89 billion individuals are social media users. People express their feelings and emotions on platforms like Twitter, Facebook, Reddit, Instagram, etc. These platforms contain valuable information which can be used for research purposes. Considerable research has been conducted across various social media platforms. However, certain limitations persist in these endeavors. Particularly, previous studies were only focused on detecting depression and the intensity of depression in tweets. Also, there existed inaccuracies in dataset labeling. In this research work, five types of depression (Bipolar, major, psychotic, atypical, and postpartum) were predicted using tweets from the Twitter database based on lexicon labeling. Explainable AI was used to provide reasoning by highlighting the parts of tweets that represent type of depression. Bidirectional Encoder Representations from Transformers (BERT) was used for feature extraction and training. Machine learning and deep learning methodologies were used to train the model. The BERT model presented the most promising results, achieving an overall accuracy of 0.96.