Automated fact‐checking: A survey

Abstract

As online false information continues to grow, automated fact‐checking has gained an increasing amount of attention in recent years. Researchers in the field of Natural Language Processing (NLP) have contributed to the task by building fact‐checking datasets, devising automated fact‐checking pipelines and proposing NLP methods to further research in the development of different components. This article reviews relevant research on automated fact‐checking covering both the claim detection and claim validation components.

CsFEVER and CTKFacts: Acquiring Czech data for fact verification

“…This section describes datasets and models related to the task of automated fact-checking of textual claims. More general overview of the state-of-the-art can be found in [2] or [9].…”

“…The omnipresence of fake news motivated formation of fact-checking organizations such as AFP Fact Check 1 , International Fact-Checking Network 2 , PolitiFact 3 , Poynter 4 , Snopes 5 , and many others. At the same time, many tools for fake news detection and fact-checking are being developed: ClaimBuster [1], ClaimReview 6 or CrowdTangle 7 ; see [2] for more examples. Many of these are based on machine learning technologies aimed at image recognition, speech to text, or Natural Language Processing (NLP).…”

“…Current state-of-the-art methods applied to the domain of automated factchecking are typically based on large-scale neural language models [5], which are notoriously data-hungry. While there is a reasonable number of quality datasets available for high-profile world languages [2], the situation for the low-resource languages is significantly less favorable. Also, most available largescale datasets are built on top of Wikipedia [4,[6][7][8].…”

Detecting and Fact-checking Misinformation using “Veracity Scanning Model”

“…However, simply detecting misinformation cannot guarantee the veracity or credibility of information. Hence, factchecking has an increasing demand for veracity scanning of information that can classify information as true or false [11]. *Corresponding Author.…”

“…*Corresponding Author. www.ijacsa.thesai.org Fact-checking is assessing the truthiness of information that is under investigation in an attempt to identify whether the information is factual [11,12]. Automatic fact-checking refers to checking the truthiness of the information repeatedly based on all available data and classifying it into True, False, Mostly True, Mostly False, and Half True.…”