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To enhance the ability to find credible evidence in news articles, we proposea novel task of expert recommendation, which aims to identify trustworthyexperts on a specific news topic. To achieve the aim, we describe theconstruction of a novel NewsQuote dataset consisting of 24,031 quote-speakerpairs that appeared on a COVID-19 news corpus. We demonstrate an automaticpipeline for speaker and quote extraction via a BERT-based Question Answeringmodel. Then, we formulate expert recommendations as document retrieval task byretrieving relevant quotes first as an intermediate step for expertidentification, and expert retrieval by directly retrieving sources based onthe probability of a query conditional on a candidate expert. Experimentalresults on NewsQuote show that document retrieval is more effective inidentifying relevant experts for a given news topic compared to expertretrieval

NewsQuote: A Dataset Built on Quote Extraction and Attribution for Expert Recommendation in Fact-Checking