Extracting a Knowledge Base of COVID-19 Events from Social Media
This provides a tool for public health researchers to monitor disease outbreaks via social media, but it is incremental as it applies existing NLP methods to a new dataset.
The paper tackles the problem of tracking COVID-19 spread by creating a manually annotated corpus of 10,000 tweets for event extraction, and shows that fine-tuning BERT-based classifiers on this corpus enables high-precision answers to complex queries like identifying organizations with positive cases in specific locations.
In this paper, 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 31 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 and help track the spread of a new disease. We also demonstrate that, by aggregating events extracted from millions of tweets, we achieve surprisingly high precision when answering complex queries, such as "Which organizations have employees that tested positive in Philadelphia?" We will release our corpus (with user-information removed), automatic extraction models, and the corresponding knowledge base to the research community.