  Fact-checking is the task of verifying the veracity of claims by assessing
their assertions against credible evidence. The vast majority of fact-checking
studies focus exclusively on political claims. Very little research explores
fact-checking for other topics, specifically subject matters for which
expertise is required. We present the first study of explainable fact-checking
for claims which require specific expertise. For our case study we choose the
setting of public health. To support this case study we construct a new dataset
PUBHEALTH of 11.8K claims accompanied by journalist crafted, gold standard
explanations (i.e., judgments) to support the fact-check labels for claims. We
explore two tasks: veracity prediction and explanation generation. We also
define and evaluate, with humans and computationally, three coherence
properties of explanation quality. Our results indicate that, by training on
in-domain data, gains can be made in explainable, automated fact-checking for
claims which require specific expertise.
