  Fact-checking has become increasingly important due to the speed with which
both information and misinformation can spread in the modern media ecosystem.
Therefore, researchers have been exploring how fact-checking can be automated,
using techniques based on natural language processing, machine learning,
knowledge representation, and databases to automatically predict the veracity
of claims. In this paper, we survey automated fact-checking stemming from
natural language processing, and discuss its connections to related tasks and
disciplines. In this process, we present an overview of existing datasets and
models, aiming to unify the various definitions given and identify common
concepts. Finally, we highlight challenges for future research.
