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