# Mining Coronavirus (COVID-19) Posts in Social Media

arXiv: https://arxiv.org/abs/2004.06778 · HTML: https://mlsanity.com/paper/2004.06778/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2020.

## Built on by (most cited first, 4 of 4)

- [Multi-View Active Learning for Short Text Classification in User-Generated Data](https://mlsanity.com/paper/2112.02611/) (2021)
- [Multiple-Source Domain Adaptation via Coordinated Domain Encoders and Paired Classifiers](https://mlsanity.com/paper/2201.11870/) (2022)
- [View Distillation with Unlabeled Data for Extracting Adverse Drug Effects from User-Generated Data](https://mlsanity.com/paper/2105.11354/) (2021)
- [Neural Networks Against (and For) Self-Training: Classification with Small Labeled and Large Unlabeled Sets](https://mlsanity.com/paper/2401.00575/) (2023)

## Abstract (arXiv)

World Health Organization (WHO) characterized the novel coronavirus (COVID-19) as a global pandemic on March 11th, 2020. Before this and in late January, more specifically on January 27th, while the majority of the infection cases were still reported in China and a few cruise ships, we began crawling social media user postings using the Twitter search API. Our goal was to leverage machine learning and linguistic tools to better understand the impact of the outbreak in China. Unlike our initial expectation to monitor a local outbreak, COVID-19 rapidly spread across the globe. In this short article we report the preliminary results of our study on automatically detecting the positive reports of COVID-19 from social media user postings using state-of-the-art machine learning models.