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Found 4 papers, 4 papers with code
Can The Crowd Identify Misinformation Objectively? The Effects of Judgment Scale and Assessor's Background
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.
The COVID-19 Infodemic: Can the Crowd Judge Recent Misinformation Objectively?
Misinformation is an ever increasing problem that is difficult to solve for the research community and has a negative impact on the society at large.
Can the Crowd Judge Truthfulness? A Longitudinal Study on Recent Misinformation about COVID-19
Our results show that: workers are able to detect and objectively categorize online (mis)information related to COVID-19; both crowdsourced and expert judgments can be transformed and aggregated to improve quality; worker background and other signals (e. g., source of information, behavior) impact the quality of the data.
The Many Dimensions of Truthfulness: Crowdsourcing Misinformation Assessments on a Multidimensional Scale
We deploy a set of quality control mechanisms to ensure that the thousands of assessments collected on 180 publicly available fact-checked statements distributed over two datasets are of adequate quality, including a custom search engine used by the crowd workers to find web pages supporting their truthfulness assessments.
