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

TL;DR

TweetsCOV19 is a comprehensive, semantically annotated knowledge base of over 8 million COVID-19 related tweets from early 2020, enabling advanced social media analysis and research.

Contribution

This work introduces a large, publicly available, semantically annotated Twitter dataset related to COVID-19, facilitating diverse computational and social science research.

Findings

The dataset covers tweets from October 2019 to April 2020.

Metadata includes entities, hashtags, sentiments, URLs, and user mentions.

Initial analysis demonstrates its utility for knowledge discovery tasks.

Abstract

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.