# Multi Class Depression Detection Through Tweets using Artificial Intelligence

arXiv: https://arxiv.org/abs/2404.13104 · HTML: https://mlsanity.com/paper/2404.13104/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 42 references that are in the map.
- First posted on arXiv 19 Apr 2024.

## Builds on (most cited first, 5 of 5)

- [Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings](https://mlsanity.com/paper/2209.07494/) (2022)
- [What Twitter Profile and Posted Images Reveal About Depression and Anxiety](https://mlsanity.com/paper/1904.02670/) (2019)
- [Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australia](https://mlsanity.com/paper/2007.02325/) (2020)
- [Data Fusion for Multi-Task Learning of Building Extraction and Height Estimation](https://mlsanity.com/paper/2308.02960/) (2023)
- [Emoji Prediction in Tweets using BERT](https://mlsanity.com/paper/2307.02054/) (2023)

## Built on by (most cited first, 2 of 2)

- [AI Models for Depressive Disorder Detection and Diagnosis: A Review](https://mlsanity.com/paper/2508.12022/) (2025)
- [When Symptoms Are Not Enough: Evidence-Weighting Patterns in Large Language Model Psychiatric Screening](https://mlsanity.com/paper/2605.23148/) (2026)

## Abstract (arXiv)

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.