TY - JOUR
T1 - CIC at CheckThat! 2022
T2 - 2022 Conference and Labs of the Evaluation Forum, CLEF 2022
AU - Arif, Muhammad
AU - Tonja, Atnafu Lambebo
AU - Ameer, Iqra
AU - Kolesnikova, Olga
AU - Gelbukh, Alexander
AU - Sidorov, Grigori
AU - Meque, Abdul Gafar Manuel
N1 - Publisher Copyright:
© 2022 Copyright for this paper by its authors.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Cross-lingual classification
KW - Fake news detection
KW - Fake news detection for low resource languages
KW - Multi-class detection
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85136985613&partnerID=8YFLogxK
M3 - Artículo de la conferencia
AN - SCOPUS:85136985613
SN - 1613-0073
VL - 3180
SP - 434
EP - 443
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
Y2 - 5 September 2022 through 8 September 2022
ER -