IDEAS home Printed from https://ideas.repec.org/a/spr/jcsosc/v8y2025i2d10.1007_s42001-024-00348-6.html

ViTHSD: exploiting hatred by targets for hate speech detection on Vietnamese social media texts

Author

Listed:
  • Cuong Nhat Vo

    (University of Information Technology
    Vietnam National University)

  • Khanh Bao Huynh

    (University of Information Technology
    Vietnam National University)

  • Son T. Luu

    (University of Information Technology
    Vietnam National University)

  • Trong-Hop Do

    (University of Information Technology
    Vietnam National University)

Abstract

The growth of social networks makes toxic content spread rapidly. Hate speech detection is a task to help decrease the number of harmful comments. With the diversity in the hate speech created by users, it is necessary to interpret the hate speech besides detecting it. Hence, we propose a methodology to construct a system for targeted hate speech detection from online streaming texts from social media. We first introduce the ViTHSD - a targeted hate speech detection dataset for Vietnamese Social Media Texts. The dataset contains 10K comments, each comment is labeled to specific targets with three levels: clean, offensive, and hate. There are 5 targets in the dataset, and each target is labeled with the corresponding level manually by humans with strict annotation guidelines. The inter-annotator agreement obtained from the dataset is 0.45 by Cohen’s Kappa index, which is indicated as a moderate level. Then, we construct a baseline for this task by combining the Bi-GRU-LSTM-CNN with the pre-trained language model to leverage the power of text representation of BERTology. Finally, we suggest a methodology to integrate the baseline model for targeted hate speech detection into the online streaming system for practical application in preventing hateful and offensive content on social media.

Suggested Citation

  • Cuong Nhat Vo & Khanh Bao Huynh & Son T. Luu & Trong-Hop Do, 2025. "ViTHSD: exploiting hatred by targets for hate speech detection on Vietnamese social media texts," Journal of Computational Social Science, Springer, vol. 8(2), pages 1-33, May.
    • Handle: RePEc:spr:jcsosc:v:8:y:2025:i:2:d:10.1007_s42001-024-00348-6
    DOI: 10.1007/s42001-024-00348-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s42001-024-00348-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s42001-024-00348-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Vaishali U. Gongane & Mousami V. Munot & Alwin D. Anuse, 2024. "A survey of explainable AI techniques for detection of fake news and hate speech on social media platforms," Journal of Computational Social Science, Springer, vol. 7(1), pages 587-623, April.
    2. Taehee Kim & Yuki Ogawa, 2024. "The impact of politicians’ behaviors on hate speech spread: hate speech adoption threshold on Twitter in Japan," Journal of Computational Social Science, Springer, vol. 7(2), pages 1161-1186, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Radu Andreea-Oana & Ionescu Sergiu-Alexandru, 2025. "Comparison of Supervised Learning Algorithms for Fake News Detection," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 19(1), pages 2139-2148.
    2. Ali Haider, Asma Junaid, 2026. "Assessing the Effectiveness of Modern Deep Learning Architectures in Multi-Label News Categorization," International Journal of Innovations in Science & Technology, 50sea, vol. 8(2), pages 546-566, April.
    3. Hossein Kermani & Mohammad Makki & Fatemeh Oudlajani & Pardis Yarahmadi & Hamideh Mahdi Soltani & Zahra HosseiniKhoo, 2026. "Incivility, domination, and resistance: rhetorical practices on Persian Twitter during the #MahsaAmini movement," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 13(1), pages 1-14, December.
    4. Kabir Ahmad, Muhammad Ali Khan, Haseena Noureen, Muhammad Fawad, Shahab Khan Umarzai, Muhammad Hammad Nawaz, 2025. "CLFT: An Optimized Hybrid Cross-Layer Fusion Transformer for Accurate Fake Profile Detection on Social Media," International Journal of Innovations in Science & Technology, 50sea, vol. 7(4), pages 3063-3081, December.
    5. AKM Bahalul Haque & Najmul Islam & Patrick Mikalef, 2025. "To Explain or Not To Explain: An Empirical Investigation of AI-based Recommendations on Social Media Platforms," Electronic Markets, Springer;IIM University of St. Gallen, vol. 35(1), pages 1-18, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jcsosc:v:8:y:2025:i:2:d:10.1007_s42001-024-00348-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.