A Survey on Automated Fact-Checking
It addresses the problem of misinformation spread for researchers and practitioners by synthesizing existing work, but is incremental as a survey.
The paper surveys automated fact-checking using natural language processing and related techniques to predict claim veracity, providing an overview of datasets, models, and definitions while identifying common concepts and future challenges.
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.