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Misinformation about critical issues such as climate change and vaccinesafety is oftentimes amplified on online social and search platforms. Thecrowdsourcing of content credibility assessment by laypeople has been proposedas one strategy to combat misinformation by attempting to replicate theassessments of experts at scale. In this work, we investigate news credibilityassessments by crowds versus experts to understand when and how ratings betweenthem differ. We gather a dataset of over 4,000 credibility assessments takenfrom 2 crowd groups---journalism students and Upwork workers---as well as 2expert groups---journalists and scientists---on a varied set of 50 newsarticles related to climate science, a topic with widespread disconnect betweenpublic opinion and expert consensus. Examining the ratings, we find differencesin performance due to the makeup of the crowd, such as rater demographics andpolitical leaning, as well as the scope of the tasks that the crowd is assignedto rate, such as the genre of the article and partisanship of the publication.Finally, we find differences between expert assessments due to differing expertcriteria that journalism versus science experts use---differences that maycontribute to crowd discrepancies, but that also suggest a way to reduce thegap by designing crowd tasks tailored to specific expert criteria. From thesefindings, we outline future research directions to better design crowdprocesses that are tailored to specific crowds and types of content.

Investigating Differences in Crowdsourced News Credibility Assessment: Raters, Tasks, and Expert Criteria