CIC at CheckThat! 2022: Multi-class and Cross-lingual Fake News Detection

Muhammad Arif, Atnafu Lambebo Tonja, Iqra Ameer, Olga Kolesnikova, Alexander Gelbukh, Grigori Sidorov, Abdul Gafar Manuel Meque

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)434-443
Number of pages10
JournalCEUR Workshop Proceedings
Volume3180
StatePublished - 2022
Event2022 Conference and Labs of the Evaluation Forum, CLEF 2022 - Bologna, Italy
Duration: Sep 5 2022Sep 8 2022

All Science Journal Classification (ASJC) codes

  • General Computer Science

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