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Sifting the Arguments in Fake News to Boost a Disinformation Analysis Tool

Jérôme Delobelle 1, 2 Amaury Delamaire 3 Elena Cabrio 2, 1 Ramón Ruti 3 Serena Villata 2, 1
1 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : The problem of disinformation spread on the Web is receiving an increasing attention, given the potential danger fake news represents for our society. Several approaches have been proposed in the literature to fight fake news, depending on the media such fake news are concerned with, i.e., text, images, or videos. Considering textual fake news, many open problems arise to go beyond simple keywords extraction based approaches. In this paper, we present a concrete application scenario where a fake news detection system is empowered with an argument mining model, to highlight and aid the analysis of the arguments put forward to support or oppose a given target topic in articles containing fake information.
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Submitted on : Thursday, November 5, 2020 - 5:02:58 PM
Last modification on : Wednesday, December 9, 2020 - 10:31:08 AM
Long-term archiving on: : Saturday, February 6, 2021 - 7:55:26 PM

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Jérôme Delobelle, Amaury Delamaire, Elena Cabrio, Ramón Ruti, Serena Villata. Sifting the Arguments in Fake News to Boost a Disinformation Analysis Tool. NL4AI 2020 - 4th Workshop on Natural Language for Artificial Intelligence, Nov 2020, Online, Italy. ⟨hal-02990781⟩

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