  Online misinformation poses a global risk with harmful implications for
society. Ordinary social media users are known to actively reply to
misinformation posts with counter-misinformation messages, which is shown to be
effective in containing the spread of misinformation. Such a practice is
defined as "social correction". Nevertheless, it remains unknown how users
respond to social correction in real-world scenarios, especially, will it have
a corrective or backfire effect on users. Investigating this research question
is pivotal for developing and refining strategies that maximize the efficacy of
social correction initiatives. To fill this gap, we conduct an in-depth study
to characterize and predict the user response to social correction in a
data-driven manner through the lens of X (Formerly Twitter), where the user
response is instantiated as the reply that is written toward a
counter-misinformation message. Particularly, we first create a novel dataset
with 55, 549 triples of misinformation tweets, counter-misinformation replies,
and responses to counter-misinformation replies, and then curate a taxonomy to
illustrate different kinds of user responses. Next, fine-grained statistical
analysis of reply linguistic and engagement features as well as repliers' user
attributes is conducted to illustrate the characteristics that are significant
in determining whether a reply will have a corrective or backfire effect.
Finally, we build a user response prediction model to identify whether a social
correction will be corrective, neutral, or have a backfire effect, which
achieves a promising F1 score of 0.816. Our work enables stakeholders to
monitor and predict user responses effectively, thus guiding the use of social
correction to maximize their corrective impact and minimize backfire effects.
The code and data is accessible on
https://github.com/claws-lab/response-to-social-correction.
