Lucas Azevedo


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LUX (Linguistic aspects Under eXamination): Discourse Analysis for Automatic Fake News Classification
Lucas Azevedo | Mathieu d’Aquin | Brian Davis | Manel Zarrouk
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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Veritas Annotator: Discovering the Origin of a Rumour
Lucas Azevedo | Mohamed Moustafa
Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)

Defined as the intentional or unintentionalspread of false information (K et al., 2019)through context and/or content manipulation,fake news has become one of the most seriousproblems associated with online information(Waldrop, 2017). Consequently, it comes asno surprise that Fake News Detection hasbecome one of the major foci of variousfields of machine learning and while machinelearning models have allowed individualsand companies to automate decision-basedprocesses that were once thought to be onlydoable by humans, it is no secret that thereal-life applications of such models are notviable without the existence of an adequatetraining dataset. In this paper we describethe Veritas Annotator, a web application formanually identifying the origin of a rumour. These rumours, often referred as claims,were previously checked for validity byFact-Checking Agencies.