Bayesian ordinal regression for crowd-sourced fact-checking

Fact-checking can be done in crowd-sourcing mode by aggregating judgments provided by many workers. We propose a Bayesian approach to carry out this aggregation, considering the truthfulness of the statements as a latent variable that underlies the ratings. We illustrate this approach and test it against alternative methods using a publicly available dataset.