Summary of Multi Class Depression Detection Through Tweets Using Artificial Intelligence, by Muhammad Osama Nusrat and Waseem Shahzad and Saad Ahmed Jamal
Multi Class Depression Detection Through Tweets using Artificial Intelligence
by Muhammad Osama Nusrat, Waseem Shahzad, Saad Ahmed Jamal
First submitted to arxiv on: 19 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the pressing issue of depression, which affects over 280 million people worldwide according to the World Health Organization (WHO). To address this problem, researchers leveraged social media platforms like Twitter, Facebook, Reddit, and Instagram to extract valuable information for research purposes. However, previous studies had limitations, focusing solely on detecting depression intensity in tweets without considering inaccuracies in dataset labeling. This paper bridges these gaps by predicting five types of depression (Bipolar, major, psychotic, atypical, and postpartum) using Twitter data based on lexicon labeling. The team employed Explainable AI to provide reasoning for their predictions, highlighting specific tweet parts that represent each type of depression. Feature extraction and training were achieved through Bidirectional Encoder Representations from Transformers (BERT), with machine learning and deep learning methodologies used to train the model. Notably, the BERT model yielded an impressive overall accuracy of 0.96. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Depression is a major problem worldwide. The World Health Organization says over 280 million people have depression. Social media platforms like Twitter, Facebook, Reddit, and Instagram can help us understand more about depression. In the past, researchers only looked at how intense someone’s depression was from their tweets. But they didn’t consider mistakes in labeling data. This study changes that by predicting five types of depression (Bipolar, major, psychotic, atypical, and postpartum) using Twitter data. The team used a special kind of AI that can explain its answers, showing which parts of the tweet are related to each type of depression. They used a powerful tool called BERT to analyze the tweets and train their model. This study had great results, with an accuracy rate of 0.96. |
Keywords
» Artificial intelligence » Bert » Deep learning » Encoder » Feature extraction » Machine learning
