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    Cyberbullying detection in Urdu language using machine learning

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    Publication date
    2023-01
    End of Embargo
    2024-01-10
    Author
    Khan, Sara
    Qureshi, Amna
    Keyword
    Cyberbullying
    Machine learning
    Natural language processing
    Twitter
    Rights
    © 2022 IEEE. Reproduced in accordance with the publisher's self-archiving policy.
    Peer-Reviewed
    Yes
    Open Access status
    embargoedAccess
    
    Metadata
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    Abstract
    Cyberbullying has become a significant problem with the surge in the use of social media. The most basic way to prevent cyberbullying on these social media platforms is to identify and remove offensive comments. However, it is hard for humans to read and remove all the comments manually. Current research work focuses on using machine learning to detect and eliminate cyberbullying. Although most of the work has been conducted on English texts to detect cyberbullying, limited to no work can be found in Urdu. This paper aims to detect cyberbullying from the users' comments posted in Urdu on Twitter using machine learning and Natural Language Processing (NLP) techniques. To the best of our knowledge, cyberbullying detection on Urdu text comments has not been performed due to the lack of a publicly available standard Urdu dataset. In this paper, we created a dataset of offensive user-generated Urdu comments from Twitter. The comments in the dataset are classified into five categories. n-gram techniques are used to extract features at character and word levels. Various supervised machine-learning techniques are applied to the dataset to detect cyberbullying. Evaluation metrics such as precision, recall, accuracy and F1 scores are used to analyse the performance of machine learning techniques.
    URI
    http://hdl.handle.net/10454/19312
    Version
    Accepted manuscript
    Citation
    Khan S and Qureshi A (2022) Cyberbullying detection in Urdu language using machine learning. From: 2022 International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE). 2-4 Dec 2022, Lahore, Pakistan.
    Link to publisher’s version
    https://doi.org/10.1109/ETECTE55893.2022.10007379
    Type
    Conference paper
    Notes
    The full-text of this article will be released for public view at the end of the publisher embargo on 10 Jan 2024.
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    Engineering and Informatics Publications

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