Private messaging platforms hinder public oversight, making misinformation hard to counter. Meanwhile, platforms are pivoting to crowdsourced verification amid waning trust in institutional fact-checkers. This raises a critical question: how do peer corrections compare with local journalists or fact-checking tiplines? We tested this via a privacy-preserving randomized field study on participants' real WhatsApp group messages in India, complemented by interviews. Fact-checks from a close contact significantly improved accuracy over the control group, while corrections from the local journalist and national tipline did not reach statistical significance. However, none of the interventions improved participants' ability to identify novel misinformation on similar themes, suggesting corrections on WhatsApp are context-bound rather than skill-building. We contribute: (1) the first ecologically valid randomized test of peer-led fact-checking on WhatsApp, benchmarked against journalists and tiplines; (2) an empirical account of how participants make sense of corrections in closed messaging environments; and (3) design implications for community-based fact-checking, including training high-social-capital individuals as embedded verifiers.
ACM CHI Conference on Human Factors in Computing Systems