  Online misinformation has been a serious threat to public health and society.
Social media users are known to reply to misinformation posts with
counter-misinformation messages, which have been shown to be effective in
curbing the spread of misinformation. This is called social correction.
However, the characteristics of tweets that attract social correction versus
those that do not remain unknown. To close the gap, we focus on answering the
following two research questions: (1) ``Given a tweet, will it be countered by
other users?'', and (2) ``If yes, what will be the magnitude of countering
it?''. This exploration will help develop mechanisms to guide users'
misinformation correction efforts and to measure disparity across users who get
corrected. In this work, we first create a novel dataset with 690,047 pairs of
misinformation tweets and counter-misinformation replies. Then, stratified
analysis of tweet linguistic and engagement features as well as tweet posters'
user attributes are conducted to illustrate the factors that are significant in
determining whether a tweet will get countered. Finally, predictive classifiers
are created to predict the likelihood of a misinformation tweet to get
countered and the degree to which that tweet will be countered. The code and
data is accessible on https://github.com/claws-lab/social-correction-twitter.
