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Threatening Expression and Target Identification in Under-Resource Languages Using NLP Techniques

  • Conference paper
  • First Online:
Analysis of Images, Social Networks and Texts (AIST 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14486))

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Abstract

In recent decades, hate speech on social media platforms has been on the rise. It is highly desired to control this kind of material because it initiates unrest and harms to the society. Literature describes several forms of the hate speech and it is quite challenging to differentiate between these forms and to design an automated detection system, especially for under-resource languages. In this study, we propose a robust framework for threatening expressions and its target identification in Urdu (Nastaliq style) language. The proposed methodology presents each step in detail like data collection & annotation, cleaning & pre-processing step, and fine-tuning of Robustly Optimized Bidirectional Encoder Representations from Transformer (Urdu-RoBERTa) with grid search technique for hyper-parameters optimization. The study exploits the strength of a pre-trained Urdu-RoBERTa as a transfer learning technique with grid search fine-tuning. The proposed framework is compared with state-of-the art baseline and ten comparable models and it outperformed all for both tasks (threatening expression and target identification). Furthermore, the proposed framework obtained benchmark performance and improved the f1-score with substantial margin.

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Acknowledgments

This article is an output of a research project implemented as part of the Basic Research Program at the National Research University Higher School of Economics (HSE University). Moreover, this research was supported in part by computational resources of HPC facilities at HSE University.

Author information

Authors and Affiliations

  1. Department of Computer Science, National Research University Higher School of Economics, 11 Pokrovskiy Boulevard, Moscow, 109028, Russian Federation

    Muhammad Shahid Iqbal Malik

Authors
  1. Muhammad Shahid Iqbal Malik
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Correspondence to Muhammad Shahid Iqbal Malik .

Editor information

Editors and Affiliations

  1. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  2. Krasovskii Institute of Mathematics and Mechanics of Russian Academy of Sciences, Yekaterinburg, Russia

    Michael Khachay

  3. University of Oslo, Oslo, Norway

    Andrey Kutuzov

  4. American University of Armenia, Yerevan, Armenia

    Habet Madoyan

  5. Artificial Intelligence Research Institute, Moscow, Russia

    Ilya Makarov

  6. University of Hamburg, Hamburg, Germany

    Irina Nikishina

  7. Skolkovo Institute of Science and Technology, Moscow, Russia

    Alexander Panchenko

  8. Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates

    Maxim Panov

  9. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  10. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  11. Apptek, Aachen, Germany

    Evgenii Tsymbalov

  12. Kazan Federal University, Kazan, Russia

    Elena Tutubalina

  13. MTS AI, Moscow, Russia

    Sergey Zagoruyko

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Malik, M.S.I. (2024). Threatening Expression and Target Identification in Under-Resource Languages Using NLP Techniques. In: Ignatov, D.I., et al. Analysis of Images, Social Networks and Texts. AIST 2023. Lecture Notes in Computer Science, vol 14486. Springer, Cham. https://doi.org/10.1007/978-3-031-54534-4_1

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  • DOI: https://doi.org/10.1007/978-3-031-54534-4_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-54533-7

  • Online ISBN: 978-3-031-54534-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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