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Truthfulness judgments are a fundamental step in the process of fightingmisinformation, as they are crucial to train and evaluate classifiers thatautomatically distinguish true and false statements. Usually such judgments aremade by experts, like journalists for political statements or medical doctorsfor medical statements. In this paper, we follow a different approach and relyon (non-expert) crowd workers. This of course leads to the following researchquestion: Can crowdsourcing be reliably used to assess the truthfulness ofinformation and to create large-scale labeled collections for informationcredibility systems? To address this issue, we present the results of anextensive study based on crowdsourcing: we collect thousands of truthfulnessassessments over two datasets, and we compare expert judgments with crowdjudgments, expressed on scales with various granularity levels. We also measurethe political bias and the cognitive background of the workers, and quantifytheir effect on the reliability of the data provided by the crowd.

Can The Crowd Identify Misinformation Objectively? The Effects of Judgment Scale and Assessor's Background