# Extracting a Knowledge Base of COVID-19 Events from Social Media

arXiv: https://arxiv.org/abs/2006.02567 · HTML: https://mlsanity.com/paper/2006.02567/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2020.

## Built on by (most cited first, 7 of 7)

- [Human-in-the-loop Evaluation for Early Misinformation Detection: A Case Study of COVID-19 Treatments](https://mlsanity.com/paper/2212.09683/) (2022)
- [Event Detection from Social Media for Epidemic Prediction](https://mlsanity.com/paper/2404.01679/) (2024)
- [SPEED++: A Multilingual Event Extraction Framework for Epidemic Prediction and Preparedness](https://mlsanity.com/paper/2410.18393/) (2024)
- [SynKB: Semantic Search for Synthetic Procedures](https://mlsanity.com/paper/2208.07400/) (2022)
- [SNaRe: Domain-aware Data Generation for Low-Resource Event Detection](https://mlsanity.com/paper/2502.17394/) (2025)
- [COVID-19 event extraction from Twitter via extractive question answering with continuous prompts](https://mlsanity.com/paper/2303.10659/) (2023)
- [How to tackle an emerging topic? Combining strong and weak labels for Covid news NER](https://mlsanity.com/paper/2209.15108/) (2022)

## Abstract (arXiv)

In this paper, we present a manually annotated corpus of 10,000 tweets containing public reports of five COVID-19 events, including positive and negative tests, deaths, denied access to testing, claimed cures and preventions. We designed slot-filling questions for each event type and annotated a total of 31 fine-grained slots, such as the location of events, recent travel, and close contacts. We show that our corpus can support fine-tuning BERT-based classifiers to automatically extract publicly reported events and help track the spread of a new disease. We also demonstrate that, by aggregating events extracted from millions of tweets, we achieve surprisingly high precision when answering complex queries, such as "Which organizations have employees that tested positive in Philadelphia?" We will release our corpus (with user-information removed), automatic extraction models, and the corresponding knowledge base to the research community.