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Information nutritional label and word embedding to estimate information check-worthiness

Lespagnol, Cédric; Mothe, Josiane; Ullah, Md Zia

Authors

Cédric Lespagnol

Josiane Mothe



Abstract

Automatic fact-checking is an important challenge nowadays since anyone can write about anything and spread it in social media, no matter the information quality. In this paper, we revisit the information check-worthiness problem and propose a method that combines the "information nutritional label" features with POS-tags and word-embedding representations. To predict the information check-worthy claim, we train a machine learning model based on these features. We experiment and evaluate the proposed approach on the CheckThat! CLEF 2018 collection. The experimental result shows that our model that combines information nutritional label and word-embedding features outperforms the baselines and the official participants' runs of CheckThat! 2018 challenge.

Presentation Conference Type Conference Paper (Published)
Conference Name 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
Start Date Jul 21, 2019
End Date Jul 25, 2019
Online Publication Date Jul 18, 2019
Publication Date 2019-07
Deposit Date Mar 13, 2023
Publisher Association for Computing Machinery (ACM)
Pages 941-944
Book Title SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
ISBN 978-1-4503-6172-9
DOI https://doi.org/10.1145/3331184.3331298