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Automatic Detection of Fake News

Pérez-Rosas, V; Kleinberg, B; Lefevre, A; Mihalcea, R; (2019) Automatic Detection of Fake News. In: Bender,, Emily M. and Derczynski,, Leon and Isabelle, Pierre, (eds.) Proceedings of the 27th International Conference on Computational Linguistics. (pp. pp. 3391-3401). Association for Computational Linguistics: Santa Fe, New Mexico, USA. Green open access

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Abstract

The proliferation of misleading information in everyday access media outlets such as social media feeds, news blogs, and online newspapers have made it challenging to identify trustworthy news sources, thus increasing the need for computational tools able to provide insights into the reliability of online content. In this paper, we focus on the automatic identification of fake content in online news. Our contribution is twofold. First, we introduce two novel datasets for the task of fake news detection, covering seven different news domains. We describe the collection, annotation, and validation process in detail and present several exploratory analysis on the identification of linguistic differences in fake and legitimate news content. Second, we conduct a set of learning experiments to build accurate fake news detectors. In addition, we provide comparative analyses of the automatic and manual identification of fake news.

Type: Proceedings paper
Title: Automatic Detection of Fake News
Event: 27th International Conference on Computational Linguistics
ISBN-13: 978-1-948087-50-6
Open access status: An open access version is available from UCL Discovery
Publisher version: https://www.aclweb.org/anthology/C18-1
Language: English
Additional information: This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http://creativecommons.org/licenses/by/4.0/.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Security and Crime Science
URI: https://discovery.ucl.ac.uk/id/eprint/10073077
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