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

Learning from Fact-checkers: Analysis and Generation of Fact-checking Language