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False information spreads on social media, and fact-checking is a potentialcountermeasure. However, there is a severe shortage of fact-checkers; anefficient way to scale fact-checking is desperately needed, especially inpandemics like COVID-19. In this study, we focus on spontaneous debunking bysocial media users, which has been missed in existing research despite itsindicated usefulness for fact-checking and countering false information.Specifically, we characterize the tweets with false information, or faketweets, that tend to be debunked and Twitter users who often debunk faketweets. For this analysis, we create a comprehensive dataset of responses tofake tweets, annotate a subset of them, and build a classification model fordetecting debunking behaviors. We find that most fake tweets are leftundebunked, spontaneous debunking is slower than other forms of responses, andspontaneous debunking exhibits partisanship in political topics. These resultsprovide actionable insights into utilizing spontaneous debunking to scaleconventional fact-checking, thereby supplementing existing research from a newperspective.

"This is Fake News": Characterizing the Spontaneous Debunking from Twitter Users to COVID-19 False Information