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

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.

References in corpus (7)

• Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set
• A large-scale COVID-19 Twitter chatter dataset for open scientific research -- an international collaboration
• Who Makes Trends? Understanding Demographic Biases in Crowdsourced Recommendations
• ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networks
• Large Arabic Twitter Dataset on COVID-19
• TweetsKB: A Public and Large-Scale RDF Corpus of Annotated Tweets
• NAIST COVID: Multilingual COVID-19 Twitter and Weibo Dataset

Cited by in corpus (5)

• A Survey of COVID-19 Misinformation: Datasets, Detection Techniques and Open Issues
• Extracting a Knowledge Base of COVID-19 Events from Social Media
• People Still Care About Facts: Twitter Users Engage More with Factual Discourse than Misinformation--A Comparison Between COVID and General Narratives on Twitter
• A Comparative Study on Transfer Learning and Distance Metrics in Semantic Clustering over the COVID-19 Tweets
• UCD-CS at W-NUT 2020 Shared Task-3: A Text to Text Approach for COVID-19 Event Extraction on Social Media