# WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets

arXiv: https://arxiv.org/abs/2010.08232 · HTML: https://mlsanity.com/paper/2010.08232/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2020.

## Built on by (most cited first, 10 of 10)

- [A Survey of Large Language Models in Medicine: Progress, Application, and Challenge](https://mlsanity.com/paper/2311.05112/) (2023)
- [The Surprising Performance of Simple Baselines for Misinformation Detection](https://mlsanity.com/paper/2104.06952/) (2021)
- [COVID-19 Named Entity Recognition for Vietnamese](https://mlsanity.com/paper/2104.03879/) (2021)
- [Large Language Model Benchmarks in Medical Tasks](https://mlsanity.com/paper/2410.21348/) (2024)
- [Evaluating Large Language Models for Health-Related Text Classification Tasks with Public Social Media Data](https://mlsanity.com/paper/2403.19031/) (2024)
- [RuNNE-2022 Shared Task: Recognizing Nested Named Entities](https://mlsanity.com/paper/2205.11159/) (2022)
- [Deep Learning for Economists](https://mlsanity.com/paper/2407.15339/) (2024)
- [Learning Mutually Informed Representations for Characters and Subwords](https://mlsanity.com/paper/2311.07853/) (2023)
- [Enhancing Coreference Resolution with Pretrained Language Models: Bridging the Gap Between Syntax and Semantics](https://mlsanity.com/paper/2504.05855/) (2025)
- [On Informative Tweet Identification For Tracking Mass Events](https://mlsanity.com/paper/2101.05656/) (2021)

## Abstract (arXiv)

In this paper, we provide an overview of the WNUT-2020 shared task on the identification of informative COVID-19 English Tweets. We describe how we construct a corpus of 10K Tweets and organize the development and evaluation phases for this task. In addition, we also present a brief summary of results obtained from the final system evaluation submissions of 55 teams, finding that (i) many systems obtain very high performance, up to 0.91 F1 score, (ii) the majority of the submissions achieve substantially higher results than the baseline fastText (Joulin et al., 2017), and (iii) fine-tuning pre-trained language models on relevant language data followed by supervised training performs well in this task.