  Automated fact checking systems have been proposed that quickly provide
veracity prediction at scale to mitigate the negative influence of fake news on
people and on public opinion. However, most studies focus on veracity
classifiers of those systems, which merely predict the truthfulness of news
articles. We posit that effective fact checking also relies on people's
understanding of the predictions. In this paper, we propose elucidating fact
checking predictions using counterfactual explanations to help people
understand why a specific piece of news was identified as fake. In this work,
generating counterfactual explanations for fake news involves three steps:
asking good questions, finding contradictions, and reasoning appropriately. We
frame this research question as contradicted entailment reasoning through
question answering (QA). We first ask questions towards the false claim and
retrieve potential answers from the relevant evidence documents. Then, we
identify the most contradictory answer to the false claim by use of an
entailment classifier. Finally, a counterfactual explanation is created using a
matched QA pair with three different counterfactual explanation forms.
Experiments are conducted on the FEVER dataset for both system and human
evaluations. Results suggest that the proposed approach generates the most
helpful explanations compared to state-of-the-art methods.
