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Impact of Transformers on Multilingual Fake News Detection for Tamil and Malayalam

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Speech and Language Technologies for Low-Resource Languages (SPELLL 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1802))

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Abstract

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).

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Notes

  1. 1.

    https://www.buzzfeednews.com/article/craigsilverman/viral-fake-election-news-outperformed-real-news-on-facebook.

  2. 2.

    https://huggingface.co/docs/tokenizers/python/latest/.

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Authors and Affiliations

  1. Department of Information Technology, National Institute of Technology Karnataka, Surathkal, India

    RamakrishnaIyer LekshmiAmmal Hariharan & Madasamy Anand Kumar

Authors
  1. RamakrishnaIyer LekshmiAmmal Hariharan
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  2. Madasamy Anand Kumar
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Correspondence to RamakrishnaIyer LekshmiAmmal Hariharan .

Editor information

Editors and Affiliations

  1. National Institute of Technology Karnataka, Mangalore, India

    Anand Kumar M

  2. National University of Ireland, Galway, Ireland

    Bharathi Raja Chakravarthi

  3. Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, India

    Bharathi B

  4. National University of Ireland, Galway, Ireland

    Colm O’Riordan

  5. Indian Institute of Technology Madras, Chennai, India

    Hema Murthy

  6. Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, India

    Thenmozhi Durairaj

  7. University of Hildesheim, Hildesheim, Germany

    Thomas Mandl

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Hariharan, R.L., Anand Kumar, M. (2023). Impact of Transformers on Multilingual Fake News Detection for Tamil and Malayalam. 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_13

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  • DOI: https://doi.org/10.1007/978-3-031-33231-9_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-33230-2

  • Online ISBN: 978-3-031-33231-9

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