Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Impact of Transformers on Multilingual Fake News Detection for Tamil and Malayalam(Springer Science and Business Media Deutschland GmbH, 2023) LekshmiAmmal, R.L.; Anand Kumar, M.Due to the availability of the technology stack for implementing state of the art neural networks, fake news or fake information classification problems have attracted many researchers working on Natural Language Processing, machine learning, and deep learning. Currently, most works on fake news detection are available in English, which has confined its widespread usability outside the English-speaking population. As far as multilingual content is considered, the fake news classification in low-resource languages is challenging due to the unavailability of enough annotated corpus. In this work, we have studied and analyzed the impact of different transformer-based models like multilingual BERT, XLMRoBERTa, and MuRIL for the dataset created (translated) as a part of this research on multilingual low-resource fake news classification. We have done various experiments, including language-specific and different models, to see the impact of the models. We also offer the multilingual dataset in Tamil and Malayalam, which are from multiple domains that could be useful for research in this direction. We have made the datasets and code available in Github (https://github.com/hariharanrl/Multilingual_Fake_News ). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Fake News Detection in Telugu Language using Transformers Models(Institute of Electrical and Electronics Engineers Inc., 2024) LekshmiAmmal, R.L.; Jinkathoti, M.; Kumar, P.S.P.; Anand Kumar, M.In today's world, lots of people rely on online news every day. But with more people using websites for information, there's a growing problem of wrong info spreading. This can make it hard to trust news, especially on social media. Detecting fake news online has become really important because it can cause problems for individuals and groups. While there's been a lot of work done on detecting fake news in popular languages, not much attention has been given to languages with fewer resources. We created a new dataset to address this issue in the detection of fake news in the Telugu language. We used different transformer models like mBERT, XLM-RoBERTa, IndicBERT, and MuRIL for fine-tuning the models in detecting fake news. MuRIL outperformed the rest of these models, obtaining an accuracy of 88.79%. MuRIL demonstrated the highest accuracy in classifying more number misleading news correctly. © 2024 IEEE.