A Survey on Automated Fact-Checking

Abstract

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.

GRAAL: Graph-Based Retrieval for Collecting Related Passages across Multiple Documents

“…More specific approaches for claim verification, a fundamental step in fact checking, are reviewed in [24]. Recent methods can rely on large annotated datasets to train machine learning models and achieve considerable results.…”

Justice in Misinformation Detection Systems: An Analysis of Algorithms, Stakeholders, and Potential Harms

“…Algorithmic misinformation detection can be composed of many sub-tasks, which some systems tackle independently while others attempt to solve in an end-to-end fashion. While the specifics of these tasks may evolve and change over time, we draw from Guo et al [34] to differentiate between three core (sequential) tasks: (1) Check-worthiness, which aims to spot factual claims that are worthy of fact-checking [11,31,39,45], (2) Evidence retrieval of potential evidence for identified claims [21,49,56,60,66,70,74] , and (3) verdict prediction, which aims to establish the veracity of a claim [60,63,74]. In a survey on the topic by Zhou and Zafarani [78], the authors identify how misinformation can be detected from four perspectives: (1) the false knowledge it carries; (2) its writing style; (3) its propagation patterns; and (4) the credibility of its source.…”