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

verfasst von: Dimitar Dimitrov, Erdal Baran, Pavlos Fafalios, Ran Yu, Xiaofei Zhu, Matthäus Zloch, Stefan Dietze
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.
Organisationseinheit(en): Forschungszentrum L3S
Externe Organisation(en): GESIS - Leibniz-Institut für Sozialwissenschaften
Foundation for Research and Technology-Hellas
Universitätsklinikum Düsseldorf
Chongqing University of Technology
Typ: Aufsatz in Konferenzband
Seiten: 2991-2998
Anzahl der Seiten: 8
Publikationsdatum: 19.10.2020
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Betriebswirtschaft, Management und Rechnungswesen (insg.), Entscheidungswissenschaften (insg.)
Elektronische Version(en): https://doi.org/10.48550/arXiv.2006.14492 (Zugang: Offen)
https://doi.org/10.1145/3340531.3412765 (Zugang: Geschlossen)

BibTeX

@inproceedings{0ea2aa79b932437883b10f2e4a217f4a,
title = "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.",
keywords = "coronavirus, covid-19, entity linking, rdf, sentiment analysis, social media archives, twitter",
author = "Dimitar Dimitrov and Erdal Baran and Pavlos Fafalios and Ran Yu and Xiaofei Zhu and Matth{\"a}us Zloch and Stefan Dietze",
year = "2020",
month = oct,
day = "19",
doi = "10.48550/arXiv.2006.14492",
language = "English",
pages = "2991--2998",
booktitle = "CIKM 2020",
publisher = "Association for Computing Machinery (ACM)",
address = "United States",
note = "29th ACM International Conference on Information and Knowledge Management, CIKM 2020 ; Conference date: 19-10-2020 Through 23-10-2020",
}