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COVID-19 search engine affiliated with Lawrence Berkeley National Laboratory (LBNL) at University of California, Berkeley: A team of materials scientists at LBNL have launched a COVID-19 publications search engine that synthesizes hundreds of scientific papers every day for information extraction using text mining algorithms and natural language processing. 

COVIDScholar.org is one of the projects awarded funding in the first round of C3.ai Digital Transformation Institute's COVID-19 initiative.

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The purpose of this work is to label tweets that are posted on Twitter about Covid19 all over the world. In the task we will show a tweet and the labeller will examine if the tweet contains a factual claim. Based on the answer (YES, NO, or Don’t know or can’t judge), the labeller might need to answer more questions.

A detailed annotation instructions with example tweets can be found here: Annotation Instructions Tutorial

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The purpose of this work is to label tweets that are posted on Twitter about Covid19 all over the world. In the task we will show a tweet and the labeller will examine if the tweet contains a factual claim. Based on the answer (YES, NO, or Don’t know or can’t judge), the labeller might need to answer more questions.

A detailed annotation instructions with example tweets can be found here: Annotation Instructions Tutorial

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