# Identifying epidemic related Tweets using noisy learning

arXiv: https://arxiv.org/abs/2209.12614 · HTML: https://mlsanity.com/paper/2209.12614/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 49 references that are in the map.
- First posted on arXiv 10 Sep 2022.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [A large-scale COVID-19 Twitter chatter dataset for open scientific research -- an international collaboration](https://mlsanity.com/paper/2004.03688/) (2020)
- [skweak: Weak Supervision Made Easy for NLP](https://mlsanity.com/paper/2104.09683/) (2021)
- [A Twitter Tale of Three Hurricanes: Harvey, Irma, and Maria](https://mlsanity.com/paper/1805.05144/) (2018)
- [Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set](https://mlsanity.com/paper/2003.07372/) (2020)
- [EPIC30M: An Epidemics Corpus Of Over 30 Million Relevant Tweets](https://mlsanity.com/paper/2006.08369/) (2020)
- [Social Media Mining Toolkit (SMMT)](https://mlsanity.com/paper/2003.13894/) (2020)
- [Tweet, but Verify: Epistemic Study of Information Verification on Twitter](https://mlsanity.com/paper/1312.5297/) (2013)

## Built on by (most cited first, 1 of 1)

- [Leveraging Large Language Models and Weak Supervision for Social Media data annotation: an evaluation using COVID-19 self-reported vaccination tweets](https://mlsanity.com/paper/2309.06503/) (2023)

## Abstract (arXiv)

Supervised learning algorithms are heavily reliant on annotated datasets to train machine learning models. However, the curation of the annotated datasets is laborious and time consuming due to the manual effort involved and has become a huge bottleneck in supervised learning. In this work, we apply the theory of noisy learning to generate weak supervision signals instead of manual annotation. We curate a noisy labeled dataset using a labeling heuristic to identify epidemic related tweets. We evaluated the performance using a large epidemic corpus and our results demonstrate that models trained with noisy data in a class imbalanced and multi-classification weak supervision setting achieved performance greater than 90%.