Developing an Efficient Toxic Comment Detector Using Machine Learning Techniques
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-56700-1_23
Reference13 articles.
1. Han, X., Tsvetkov, Y.: Fortifying toxic speech detectors against veiled toxicity. In: EMNLP 2020–2020 Proceedings of the Conference on Empirical Methods in Natural Language Processing Conference (EMNLP), pp. 7732–7739 (2020). https://doi.org/10.18653/v1/2020.emnlp-main.622
2. Karan, M., Šnajder, J.: Preemptive Toxic Language Detection in Wikipedia Comments Using Thread-Level Context, pp. 129–134, September 2019. https://doi.org/10.18653/V1/W19-3514
3. David, F., Guimarães, N., Figueira, Á.: A WebApp for reliability detection in social media. Procedia Comput. Sci. 219, 228–235 (2023). https://doi.org/10.1016/j.procs.2023.01.285
4. Cherradi, B., Rachidi, R., Ouassil, M.A., Errami, M., Hamida, S., Silkan, H.: Classifying toxicity in the Arabic Moroccan dialect on Instagram: a machine and deep learning approach. Artic. Indones. J. Electr. Eng. Comput. Sci. 31(1), 588–598 (2023). https://doi.org/10.11591/ijeecs.v31.i1.pp588-598
5. Obadimu, A., Mead, E., Hussain, M.N., Agarwal, N.: Identifying toxicity within YouTube video comment. In: Thomson, R., Bisgin, H., Dancy, C., Hyder, A. (eds.) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2019. LNCS, vol. 11549, pp. 214–223. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21741-9_22/COVER
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