Factchecking Matters: The Value of Crowdsourcing for Enhanced Accuracy Judgments

This research project proposes a method to leverage the input of the general population (crowdsourcing), algorithms (supervised learning), and experts (third-party checkers) to detect false information on news media. The principal investigator proposes the use of similarity judgments to facilitate unbiased responses that can be used to predict untrustworthy arguments.