Authors:

Lucas Azevedo, Mohamed Moustafa

Publication Type:

Refereed Conference Meeting Proceeding

Abstract:
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.

Conference Name:

EMNLP

Proceedings:

Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)

Digital Object Identifer (DOI):

10.18653/v1/D19-6614

Publication Date:

09/11/2019

Conference Location:

Hong Kong

Research Group:

Machine Learning & Statistics

Institution:

National University of Ireland, Galway (NUIG)

Open access repository:

Yes

https://www.aclweb.org/anthology/D19-6614.pdf

Publication document:

PDF icon fever.pdf