  In fighting against fake news, many fact-checking systems comprised of
human-based fact-checking sites (e.g., snopes.com and politifact.com) and
automatic detection systems have been developed in recent years. However,
online users still keep sharing fake news even when it has been debunked. It
means that early fake news detection may be insufficient and we need another
complementary approach to mitigate the spread of misinformation. In this paper,
we introduce a novel application of text generation for combating fake news. In
particular, we (1) leverage online users named \emph{fact-checkers}, who cite
fact-checking sites as credible evidences to fact-check information in public
discourse; (2) analyze linguistic characteristics of fact-checking tweets; and
(3) propose and build a deep learning framework to generate responses with
fact-checking intention to increase the fact-checkers' engagement in
fact-checking activities. Our analysis reveals that the fact-checkers tend to
refute misinformation and use formal language (e.g. few swear words and
Internet slangs). Our framework successfully generates relevant responses, and
outperforms competing models by achieving up to 30\% improvements. Our
qualitative study also confirms that the superiority of our generated responses
compared with responses generated from the existing models.
