Abstract

Nowadays, social media is one widely used platform to access information. Fake news on social media and various other media is widely spreading. It is a matter of serious concern due to its ability to cause a lot of social and national damage with destructive impacts. Therefore, detecting misleading news is critical to detect automatically. Fake news detection software has been used in a variety of fields, such as social media, health, political news, etc. This paper presents the Instituto Politécnico Nacional (Mexico) at CheckThat! 2022. In this paper, we discuss using different algorithms for the multiclass and cross-lingual fake news detection task. We achieved a macro F1-score of 28.60% for a mono-lingual task in English (task 3a) using RoBERTa pre-trained model and 17.21% for a cross-lingual task for English and German (task 3b) using Bi-LSTM deep learning algorithm.

Keywords

Fake news detection, Cross-lingual classification, Multi-class detection, Fake news detection for low resource languages, Transfer learning

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Recommended citation:

@inproceedings{ArifEtAl:CLEF2022,
title = {CIC at CheckThat! 2022: Multi-class and Cross-lingual Fake News Detection},
author = {Muhammad Arif and Atnafu Lambebo Tonja and Iqra Ameer and Olga Kolesnikova and Alexander Gelbukh and Grigori Sidorov and Abdul Gafar Manuel Meque},
pages = {434--443},
url = {http://ceur-ws.org/Vol-3180/#paper-33},
crossref = {CLEF2022},
}