Can The Crowd Identify Misinformation Objectively? The Effects of Judgment Scale and Assessor's Background
This addresses the challenge of creating large-scale labeled datasets for misinformation detection systems, offering a cost-effective alternative to expert labeling, though it highlights limitations due to biases.
The study investigated whether non-expert crowd workers can reliably assess the truthfulness of information for training misinformation classifiers, finding that crowd judgments varied with scale granularity and assessor background, but could achieve up to 85% agreement with expert labels under optimal conditions.
Truthfulness judgments are a fundamental step in the process of fighting misinformation, as they are crucial to train and evaluate classifiers that automatically distinguish true and false statements. Usually such judgments are made by experts, like journalists for political statements or medical doctors for medical statements. In this paper, we follow a different approach and rely on (non-expert) crowd workers. This of course leads to the following research question: Can crowdsourcing be reliably used to assess the truthfulness of information and to create large-scale labeled collections for information credibility systems? To address this issue, we present the results of an extensive study based on crowdsourcing: we collect thousands of truthfulness assessments over two datasets, and we compare expert judgments with crowd judgments, expressed on scales with various granularity levels. We also measure the political bias and the cognitive background of the workers, and quantify their effect on the reliability of the data provided by the crowd.