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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
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ISO Certification: 9001:2015
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License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 04

Published on: April 2026

YOUTUBE TOXIC COMMENT CLASSIFICATION

R. Maidhili S. Devi Yashoda R. Ravi Teja Aakash Beshra

T. Swathi

Department of CSE(Data Science) ACE Engineering CollegeHyderabad Telangana India
DOI:https://doi.org/10.55041/ijcope.v2i4.240

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Plagiarism Passed Peer Reviewed Open Access

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Abstract

The rapid expansion of user-generated content on digital platforms, particularly YouTube, has significantly transformed the way people communicate and share opinions online. However, this growth has also led to a substantial rise in toxic comments, including insults, threats, abusive language, obscenity, and identity-based hate speech. Such content not only harms individuals but also disrupts healthy online discussions and creates an unsafe digital environment for users. Traditionally, moderation of comments has relied heavily on manual efforts, where human moderators review and filter inappropriate content. While this approach can be effective to some extent, it is highly time-consuming, labor-intensive, and impractical for handling the massive volume of comments generated every second on large platforms. As a result, there is a growing need for an automated and intelligent system that can efficiently detect and filter toxic content in real time, ensuring a safer and more respectful online community. To address this challenge, machine learning techniques can be employed for automated text classification. In this approach, raw textual data from comments is first preprocessed through cleaning steps such as removing punctuation, stopwords, and irrelevant characters.

 

How to Cite this Paper

Maidhili, R., Yashoda, S. D., Teja, R. R. & Beshra, A. (2026). YouTube Toxic Comment Classification. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.240

Maidhili, R., et al.. "YouTube Toxic Comment Classification." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.240.

Maidhili, R.,S. Yashoda,R. Teja, and Aakash Beshra. "YouTube Toxic Comment Classification." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.240.

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References


  • Jigsaw & Google (2018). Toxic Comment Classification Challenge Dataset. Platform: Kaggle Link:https://www.kaggle.com/c/jigsaw-toxic- comment-classification-challenge



  • Davidson, T., Warmsley, D., Macy, M., & Weber, I. (2017). Automated Hate Speech Detection and the  Problem  of  OffensiveLanguage. Journal: Proceedings of ICWSM (International AAAI Conference on Web and Social Media)



  • Schmidt, , & Wiegand, M. (2017). A Survey on Hate Speech Detection using Natural Language Processing. Journal: Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media

  • Devlin, , Chang, M. W., Lee, K., & Toutanova,(2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Journal: NAACL-HLT DOI: 10.48550/arXiv.1810.04805



  • Zhang, Z., Robinson, D., & Tepper, J. (2018). Detecting Hate Speech on Twitter Using a ConvolutionGRU Based Deep Neural Network. Journal: European Semantic Web Conference (ESWC)

  • Pedregosa, F., et al. (2011). Scikit-learn: Machine Learning in Journal: Journal of Machine Learning Research (JMLR) DOI: 10.5555/1953048.2078195

  • Salton, G., & Buckley, C. (1988). Term- Weighting Approaches in Automatic Text Retrieval (TF-IDF). Journal: Information Processing & Management DOI: 1016/0306- 4573(88)90021-0

  • Jurafsky, D., & Martin, J. H. (2020). Speech and Language Processing. Publisher: Pearson (Book Reference)

  • Aggarwal, C. (2018). Machine Learning for Text Mining. Publisher: Springer 10. Kowsari, K., et al. (2019). Text Classification Algorithms: A Survey. Journal: Information DOI: 10.3390/info10040150

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  • •All submissions are screened under plagiarism detection.
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  • •Peer Review Type: Double-Blind Peer Review
  • •Published on: Apr 12 2026
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