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

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

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.

Citation

Lespagnol, C., Mothe, J., & Ullah, M. Z. (2019, July). Information nutritional label and word embedding to estimate information check-worthiness. Presented at 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Paris

• 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