TY - JOUR
T1 - Automatic Fake News Detection in Urdu Language using Transformers
AU - Ameer, Iqra
AU - Capetillo, Claudia Porto
AU - Gómez-Adorno, Helena
AU - Sidorov, Grigori
N1 - Publisher Copyright:
© 2021 Copyright for this paper by its authors.
PY - 2021
Y1 - 2021
N2 - Due to easy access to the internet, the content on social media increased drastically. It is easy to write or spread anything on the web without taking care of the trustfulness of the source. Fake news is now a whole society’s problem, sometimes fakes news spread faster than real news. It has adverse effects on people and firms. This makes automatic fake news detection an essential task. Automatic fake news detection has been using in different domains, including social media posts, health, and well-being news, political news, etc. This paper presents the Instituto Politécnico Nacional (Mexico) at FIRE 20211 for Urdu language fake news detection shared task [1, 2]. This paper aims to detect fake news on Urdu fake news articles belongs to six different domains, i.e., business, health, showbiz, sports, and technology. In the proposed approach, we applied the state-of-the-art transfer learning algorithm BERT. The best result of 0.91 (see Table 3) is obtained when we trained and validated our model before predictions on the test set. We submitted two different runs of the BERT model in this shared task. Our systems achieved 0.66 accuracy on the unlabeled test dataset provided to evaluate the submitted systems.
AB - Due to easy access to the internet, the content on social media increased drastically. It is easy to write or spread anything on the web without taking care of the trustfulness of the source. Fake news is now a whole society’s problem, sometimes fakes news spread faster than real news. It has adverse effects on people and firms. This makes automatic fake news detection an essential task. Automatic fake news detection has been using in different domains, including social media posts, health, and well-being news, political news, etc. This paper presents the Instituto Politécnico Nacional (Mexico) at FIRE 20211 for Urdu language fake news detection shared task [1, 2]. This paper aims to detect fake news on Urdu fake news articles belongs to six different domains, i.e., business, health, showbiz, sports, and technology. In the proposed approach, we applied the state-of-the-art transfer learning algorithm BERT. The best result of 0.91 (see Table 3) is obtained when we trained and validated our model before predictions on the test set. We submitted two different runs of the BERT model in this shared task. Our systems achieved 0.66 accuracy on the unlabeled test dataset provided to evaluate the submitted systems.
KW - BERT
KW - Classification
KW - Fake news
KW - Transfer learning
KW - Urdu language
UR - http://www.scopus.com/inward/record.url?scp=85134239515&partnerID=8YFLogxK
M3 - Artículo de la conferencia
AN - SCOPUS:85134239515
SN - 1613-0073
VL - 3159
SP - 1127
EP - 1134
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
T2 - Working Notes of FIRE - 13th Forum for Information Retrieval Evaluation, FIRE-WN 2021
Y2 - 13 December 2021 through 17 December 2021
ER -