We propose and test a crowd-based content moderation approach to combat the spread of misinformation. In this method, users can flag posts they believe contain misinformation, with such flags serving as visible cues to others. These cues then influence users' moderation and sharing decisions. We assess users' willingness to participate in moderation and identify the underlying drivers. Thereafter, we examine whether such flags can address two primary causes for the spread of misinformation -lack of knowledge and scrutiny, specifically when posts align with one's ideology. We developed a social media application and conducted a randomized controlled experiment. Participants were shown both true and false posts about politics, COVID-19, and the Russia-Ukraine conflict, along with social cues like flags and share counts. Our results demonstrate that users with a higher level of knowledge are more likely to flag misinformation and that their flags are not influenced by their personal beliefs. The presence of these flags encourages other users, especially those with less information, to be more cautious about sharing content. The presence of flags also reduces the spread of misinformation, even when users' beliefs align with false posts. We also establish that users' flagging and sharing behavior are driven by their updated opinions regarding the accuracy of the post and the need for impression management. Our study reveals a key limitation in the current approach to platform moderation, where user feedback is not made visible to others. It demonstrates that a crowd-based approach can be effective in curbing misinformation.