# TweetsCOV19 -- A Knowledge Base of Semantically Annotated Tweets about the COVID-19 Pandemic

arXiv: https://arxiv.org/abs/2006.14492 · HTML: https://mlsanity.com/paper/2006.14492/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2020.

## Built on by (most cited first, 9 of 9)

- [Human-in-the-loop Evaluation for Early Misinformation Detection: A Case Study of COVID-19 Treatments](https://mlsanity.com/paper/2212.09683/) (2022)
- [SciTweets -- A Dataset and Annotation Framework for Detecting Scientific Online Discourse](https://mlsanity.com/paper/2206.07360/) (2022)
- [MM-Claims: A Dataset for Multimodal Claim Detection in Social Media](https://mlsanity.com/paper/2205.01989/) (2022)
- [Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims](https://mlsanity.com/paper/2205.02596/) (2022)
- [An Analysis of COVID-19 Knowledge Graph Construction and Applications](https://mlsanity.com/paper/2110.04932/) (2021)
- [A Comparative Study on Transfer Learning and Distance Metrics in Semantic Clustering over the COVID-19 Tweets](https://mlsanity.com/paper/2111.08658/) (2021)
- [MigrationsKB: A Knowledge Base of Public Attitudes towards Migrations and their Driving Factors](https://mlsanity.com/paper/2108.07593/) (2021)
- [DS@GT at CheckThat! 2025: Ensemble Methods for Detection of Scientific Discourse on Social Media](https://mlsanity.com/paper/2507.06205/) (2025)
- [CrisisKD: Five-Stage Knowledge Distillation for Aspect-Level Sentiment and Emotion Analysis in Crisis Discourse](https://mlsanity.com/paper/2609.05757/) (2026)

## Abstract (arXiv)

Publicly available social media archives facilitate research in the social sciences and provide corpora for training and testing a wide range of machine learning and natural language processing methods. With respect to the recent outbreak of the Coronavirus disease 2019 (COVID-19), online discourse on Twitter reflects public opinion and perception related to the pandemic itself as well as mitigating measures and their societal impact. Understanding such discourse, its evolution, and interdependencies with real-world events or (mis)information can foster valuable insights. On the other hand, such corpora are crucial facilitators for computational methods addressing tasks such as sentiment analysis, event detection, or entity recognition. However, obtaining, archiving, and semantically annotating large amounts of tweets is costly. In this paper, we describe TweetsCOV19, a publicly available knowledge base of currently more than 8 million tweets, spanning October 2019 - April 2020. Metadata about the tweets as well as extracted entities, hashtags, user mentions, sentiments, and URLs are exposed using established RDF/S vocabularies, providing an unprecedented knowledge base for a range of knowledge discovery tasks. Next to a description of the dataset and its extraction and annotation process, we present an initial analysis and use cases of the corpus.