Abstract
Fake news spreads much faster than real news. False information and misleading texts are the most important elements that lead to disasters and even life threats. One such strategy is fake news, which has become a never-ending phenomenon with the rise of the internet. There can be several devastating consequences due to fake news spreading. It is therefore important to prevent the spread of fake news. This paper shows how we prepared fake news data sets for a few low-resource languages and how we used Logistic Regression and BERT models to perform fake news classification in low-resource languages. Through rigorous experiments, we show that BERT-based-multilingual-cased and Logistic Regression models reach maximum F1 scores of around 98% and 95% respectively. We have done fake news classification with the models for low-resource Indian languages like Tamil, Kannada, Gujarati, and Malayalam.
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Sivanaiah, R., Ramanathan, N., Hameed, S., Rajagopalan, R., Suseelan, A.D., Thanagathai, M.T.N. (2023). Fake News Detection in Low-Resource Languages. In: M, A.K., et al. Speech and Language Technologies for Low-Resource Languages . SPELLL 2022. Communications in Computer and Information Science, vol 1802. Springer, Cham. https://doi.org/10.1007/978-3-031-33231-9_23
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DOI: https://doi.org/10.1007/978-3-031-33231-9_23
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