  A reasonable approach for fact checking a claim involves retrieving
potentially relevant documents from different sources (e.g., news websites,
social media, etc.), determining the stance of each document with respect to
the claim, and finally making a prediction about the claim's factuality by
aggregating the strength of the stances, while taking the reliability of the
source into account. Moreover, a fact checking system should be able to explain
its decision by providing relevant extracts (rationales) from the documents.
Yet, this setup is not directly supported by existing datasets, which treat
fact checking, document retrieval, source credibility, stance detection and
rationale extraction as independent tasks. In this paper, we support the
interdependencies between these tasks as annotations in the same corpus. We
implement this setup on an Arabic fact checking corpus, the first of its kind.
