Factchecking Matters: The Value of Crowdsourcing for Enhanced Accuracy Judgments

Awarded Scholars:
Yu Ding , Columbia University
Project Date:
Jun 2020
Award Amount:
$9,940

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 investigatorĀ proposes the use of similarity judgments to facilitate unbiased responses that can be used to predict untrustworthy arguments.

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