Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | CIKM 2020 |
| Untertitel | Proceedings of the 29th ACM International Conference on Information and Knowledge Management |
| Herausgeber (Verlag) | Association for Computing Machinery (ACM) |
| Seiten | 2991-2998 |
| Seitenumfang | 8 |
| ISBN (elektronisch) | 978-1-4503-6859-9 |
| Publikationsstatus | Veröffentlicht - 19 Okt. 2020 |
| Veranstaltung | 29th ACM International Conference on Information and Knowledge Management - online, Virtual, Online, Irland Dauer: 19 Okt. 2020 → 23 Okt. 2020 |
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.
ASJC Scopus Sachgebiete
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
CIKM 2020: Proceedings of the 29th ACM International Conference on Information and Knowledge Management. Association for Computing Machinery (ACM), 2020. S. 2991-2998.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - TweetsCOV19 - A Knowledge Base of Semantically Annotated Tweets about the COVID-19 Pandemic
AU - Dimitrov, Dimitar
AU - Baran, Erdal
AU - Fafalios, Pavlos
AU - Yu, Ran
AU - Zhu, Xiaofei
AU - Zloch, Matthäus
AU - Dietze, Stefan
PY - 2020/10/19
Y1 - 2020/10/19
N2 - 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.
AB - 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.
KW - coronavirus
KW - covid-19
KW - entity linking
KW - rdf
KW - sentiment analysis
KW - social media archives
KW - twitter
UR - http://www.scopus.com/inward/record.url?scp=85091968534&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2006.14492
DO - 10.48550/arXiv.2006.14492
M3 - Conference contribution
AN - SCOPUS:85091968534
SP - 2991
EP - 2998
BT - CIKM 2020
PB - Association for Computing Machinery (ACM)
T2 - 29th ACM International Conference on Information and Knowledge Management, CIKM 2020
Y2 - 19 October 2020 through 23 October 2020
ER -