Summary of Covidhealth: a Benchmark Twitter Dataset and Machine Learning Based Web Application For Classifying Covid-19 Discussions, by Mahathir Mohammad Bishal et al.
COVIDHealth: A Benchmark Twitter Dataset and Machine Learning based Web Application for Classifying COVID-19 Discussions
by Mahathir Mohammad Bishal, Md. Rakibul Hassan Chowdory, Anik Das, Muhammad Ashad Kabir
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI)
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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 The paper proposes a machine learning-based web application for automatically categorizing COVID-19-related discussions on social media. The authors label Twitter data and provide benchmark classification results using various traditional and deep learning algorithms. They achieve a maximum F1 score of 90.43% with the CNN algorithm in deep learning, outperforming other approaches. The paper contributes to health-related data analysis and provides a publicly available web-based tool for efficient data classification, which can aid in addressing public health challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a computer program that helps sort COVID-19 conversations on social media into different categories like health risks or symptoms. They used Twitter data and tested many machine learning algorithms to find the best one. The best algorithm was called CNN and it got 90% of the answers correct! This is helpful for understanding what people are saying about COVID-19 online, which can help us fight the pandemic. |
Keywords
* Artificial intelligence * Classification * Cnn * Deep learning * F1 score * Machine learning
