Learning Deep Fake-News Detectors from Scarcely-Labelled News Corpora

P. Zicari, M. Guarascio, L. Pontieri, G. Folino

2023

Abstract

Nowadays, news can be rapidly published and shared through several different channels (e.g., Twitter, Facebook, Instagram, etc.) and reach every person worldwide. However, this information is typically unverified and/or interpreted according to the point of view of the publisher. Consequently, malicious users can leverage these unofficial channels to share misleading or false news to manipulate the opinion of the readers and make fake news viral. In this scenario, early detection of this malicious information is challenging as it requires coping with several issues (e.g., scarcity of labelled data, unbalanced class distribution, and efficient handling of raw data). To address all these issues, in this work, we propose a Semi-Supervised Deep Learning based approach that allows for discovering accurate and effective Fake News Detection models. By embedding a BERT model in a pseudo-labelling procedure, the approach can yield reliable detection models also when a limited number of examples are available. Extensive experimentation on two benchmark datasets demonstrates the quality of the proposed solution.

Paper Citation

in Harvard Style

Zicari P., Guarascio M., Pontieri L. and Folino G. (2023). Learning Deep Fake-News Detectors from Scarcely-Labelled News Corpora. In Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-648-4, SciTePress, pages 344-353. DOI: 10.5220/0011827500003467

in Bibtex Style

@conference{iceis23,
author={P. Zicari and M. Guarascio and L. Pontieri and G. Folino},
title={Learning Deep Fake-News Detectors from Scarcely-Labelled News Corpora},
booktitle={Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2023},
pages={344-353},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011827500003467},
isbn={978-989-758-648-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Learning Deep Fake-News Detectors from Scarcely-Labelled News Corpora
SN - 978-989-758-648-4
AU - Zicari P.
AU - Guarascio M.
AU - Pontieri L.
AU - Folino G.
PY - 2023
SP - 344
EP - 353
DO - 10.5220/0011827500003467
PB - SciTePress