Extracting a Knowledge Base of COVID-19 Events from Social Media
Published in
International Conference on Computational Lingu...(2020)External Links:
TL
TL;DR
A manually annotated corpus of 10,000 tweets containing public reports of five COVID-19 events is presented, showing that the corpus can support fine-tuning BERT-based classifiers to automatically extract publicly reported events, which can be further collected for building a knowledge base.
Abstract
We present a manually annotated corpus of 10,000 tweets containing public reports of five COVID-19 events, including positive and negative tests, deaths, denied access to testing, claimed cures and preventions. We designed slot-filling questions for each event type and annotated a total of 28 fine-grained slots, such as the location of events, recent travel, and close contacts. We show that our corpus can support fine-tuning BERT-based classifiers to automatically extract publicly reported events, which can be further collected for building a knowledge base. Our knowledge base is constructed over Twitter data covering two years and currently covers over 4.2M events. It can answer complex queries with high precision, such as “Which organizations have employees that tested positive in Philadelphia?” We believe our proposed methodology could be quickly applied to develop knowledge bases for new domains in response to an emerging crisis, including natural disasters or future disease outbreaks.
References45 items
1
Event-Related Bias Removal for Real-time Disaster Events
2
WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets
3
Extracting a Knowledge Base of Mechanisms from COVID-19 Papers
4
COVIDLies: Detecting COVID-19 Misinformation on Social Media
5