ClaimBuster: The First-ever End-to-end Fact-checking System

By Naeemul Hassan, Gensheng Zhang, Fatma Arslan, Josue Caraballo, Damian Jimenez, Siddhant Gawsane, Shohedul Hasan, M. Joseph, Aaditya Kulkarni, Anil Kumar Nayak and 3 more. 2017

298 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Sociology and Political Science, Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

Internet-Augmented Dialogue Generation (2021, background): “While issues of toxicity can perhaps be treated similarly to the pre-training data case (e.g. safety classi-ﬁers), fact checking is a separate area with ongoing work, e.g. Hassan et al. (2017); Fan et al. (2020).”
A Survey of Fake News (2020, background): “…in many ways, such as by verifying if the title of the news article is a clickbait [Zhou et al. 2019a], if its content will trigger widespread discussions on social media [Vosoughi et al. 2018], or if its topic relates to national affairs and matches with public concerns [Hassan et al. 2017a].”
FANG: Leveraging Social Context for Fake News Detection Using Graph Representation (2020, background): “uch as Snopes, FactCheck, PolitiFact, and Full Fact. In order to scale with the increasing amount of information, automated news verification systems consider external knowledge databases as evidence [13, 34, 42]. Evidence-based approaches achieve high accuracy and offer potential explainability, bu”
User Preference-aware Fake News Detection (2021, background): “Among various fake news detection techniques, fact-checking is the most straightforward approach; however, it is usually laborintensive to acquire evidence from domain experts [9].”