  False information spreads on social media, and fact-checking is a potential
countermeasure. However, there is a severe shortage of fact-checkers; an
efficient way to scale fact-checking is desperately needed, especially in
pandemics like COVID-19. In this study, we focus on spontaneous debunking by
social media users, which has been missed in existing research despite its
indicated usefulness for fact-checking and countering false information.
Specifically, we characterize the tweets with false information, or fake
tweets, that tend to be debunked and Twitter users who often debunk fake
tweets. For this analysis, we create a comprehensive dataset of responses to
fake tweets, annotate a subset of them, and build a classification model for
detecting debunking behaviors. We find that most fake tweets are left
undebunked, spontaneous debunking is slower than other forms of responses, and
spontaneous debunking exhibits partisanship in political topics. These results
provide actionable insights into utilizing spontaneous debunking to scale
conventional fact-checking, thereby supplementing existing research from a new
perspective.
