Advancement in Automated Hate Speech Detection: A Survey of Machine Learning and Ensemble Methods

Author: Rutuja Goradkar,Smita Bharne,Vaibhav Narawade

Published Online: July 1, 2026

DOI: http://doi.org/10.63766/spujstmr.26.000089

Abstract

References

This survey article presents a comprehensive review of state-of-the-art methodologies for real-time hate speech and toxicity detection on social media platforms, with a focus on their contributions and associated performance metrics. The survey mainly focused on classical machine learning models such as Support Vector Machine (SVM), Random Forest, XGBoost, Logistic Regression, and ensemble learning strategies, which have shown improvements in classification accuracy by leveraging complementary strengths. In addition, deep learning approaches, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) models, as well as transformer-based architectures such as BERT and RoBERTa, are reviewed for their ability to capture contextual and sequential patterns that traditional ML methods often miss. The survey also shows the role of stacking models in enhancing performance by achieving superior accuracy compared to individual classifiers. The revised survey incorporates a formal methodology section, a taxonomy of detection methods, a dedicated dataset comparison, coverage of advanced models including DeBERTa [24], HateBERT [26], LLM-based [27] and multimodal approaches [28], strengthened critical analysis, and a future research directions section.

[1] M. A. Hisseine, D. Chen, and X. Yang, “The Application of Blockchain in Social Media: A Systematic Literature Review,” Appl. Sci., vol. 12, no. 13, p. 6567, June 2022, doi: 10.3390/app12136567.

[2] T. Iqbal, M. Khan, K. Taveter, and N. Seyff, “Mining Reddit as a New Source for Software Requirements,” in 2021 IEEE 29th International Requirements Engineering Conference (RE), Notre Dame, IN, USA: IEEE, Sept. 2021, pp. 128–138. doi: 10.1109/RE51729.2021.00019.

[3] Anjum and R. Katarya, “Hate speech, toxicity detection in online social media: a recent survey of state of the art andopportunities,” Int. J. Inf. Secur., vol. 23, no. 1, pp. 577–608, Feb. 2024, doi: 10.1007/s10207-023-00755-2.

[4] T. Garg, S. Masud, T. Suresh, and T. Chakraborty, “Handling Bias in Toxic Speech Detection: A Survey,” ACM Comput. Surv., vol. 55, no. 13s, pp. 1–32, Dec. 2023, doi: 10.1145/3580494.

[5] C. Arcila-Calderón, J. J. Amores, P. Sánchez-Holgado, and D. Blanco-Herrero, “Using Shallow and Deep Learning to Automatically Detect Hate Motivated by Gender and Sexual Orientation on Twitter in Spanish,” Multimodal Technol. Interact., vol. 5, no. 10, p. 63, Oct. 2021, doi: 10.3390/mti5100063.

[6] O. Stoika and V. Pitоvka, “Social media and language innovation: a study of slang, abbreviations, and emoticons,” СучасніДослідження З Іноземної Філології, no. 1(27), pp. 226–238, 2025, doi: 10.32782/2617-3921.2025.27.226-238.

[7] H. Zhang and M. O. Shafiq, “Survey of transformers and towards ensemble learning using transformers for natural language processing,” J. Big Data, vol. 11, no. 1, p. 25, Feb. 2024, doi: 10.1186/s40537-023-00842-0.

[8] M. B. Shaik and D. S. Rao, “A Comprehensive Review of the Literature and Meta-analysis of Approaches and Datasets for Investigating Textual Hate Speech Identification,” in Intelligent Computing and Communication, vol. 1241, K. S. Raju, R. Senkerik, T. K. Kumar, M. Sellathurai, and V. Naresh Kumar, Eds., in Lecture Notes in Networks and Systems, vol. 1241. , Singapore: Springer Nature Singapore, 2025, pp. 527–544. doi: 10.1007/978-981-96-1267-3_43.

[9] N. S. Mullah and W. M. N. W. Zainon, “Advances in Machine Learning Algorithms for Hate Speech Detection in Social Media: A Review,” IEEE Access, vol. 9, pp. 88364–88376, 2021, doi: 10.1109/ACCESS.2021.3089515.

[10] A. Toktarovaet al., “Hate Speech Detection in Social Networks using Machine Learning and Deep Learning Methods,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 5, 2023, doi: 10.14569/IJACSA.2023.0140542.

[11] I. Aljarahet al., “Intelligent detection of hate speech in Arabic social network: A machine learning approach,” J. Inf. Sci., vol. 47, no. 4, pp. 483–501, Aug. 2021, doi: 10.1177/0165551520917651.

[12] S. Abro, S. Shaikh, Z. Hussain, Z. Ali, S. Khan, and G. Mujtaba, “Automatic Hate Speech Detection using Machine Learning: A Comparative Study,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 8, 2020, doi: 10.14569/IJACSA.2020.0110861.

[13] S. Das, K. Bhattacharyya, and S. Sarkar, “Performance Analysis of Logistic Regression, Naive Bayes, KNN, Decision Tree, Random Forest and SVM on Hate Speech Detection from Twitter,” Int. Res. J. Innov.Eng. Technol., vol. 07, no. 03, pp. 07–03, 2023, doi: 10.47001/IRJIET/2023.703004

[14]A. Silva and N. Roman, “Hate Speech Detection in Portuguese with Naïve Bayes, SVM, MLP and Logistic Regression,” in Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2020), Brasil: Sociedade Brasileira de Computação - SBC, Oct. 2020, pp. 1–12. doi: 10.5753/eniac.2020.12112.

[15] Y. V. S. Rohith and M. Amanullah, “Improving the Accuracy by Comparing the Gaussian Naive Bayes Algorithm and Logistic Regression for Predicting Hate Speech Recognition,” in 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kamand, India: IEEE, June 2024, pp. 1–5. doi: 10.1109/ICCCNT61001.2024.10723838.

[16] N. P. Damayanti, D. E. Prameswari, W. Puspita, and P. S. Sundari, “Classification of Hate Comments on Twitter Using a Combination of Logistic Regression and Support Vector Machine Algorithm,” J. Inf. Syst. Explor. Res., vol. 2, no. 1, Jan. 2024, doi: 10.52465/joiser.v2i1.229.

[17] P. K. Roy, A. K. Tripathy, T. K. Das, and X.-Z. Gao, “A Framework for Hate Speech Detection Using Deep Convolutional Neural Network,” IEEE Access, vol. 8, pp. 204951–204962, 2020, doi: 10.1109/ACCESS.2020.3037073.

[18] M. U. S. Khan, A. Abbas, A. Rehman, and R. Nawaz, “HateClassify: A Service Framework for Hate Speech Identification on Social Media,” IEEE Internet Comput., vol. 25, no. 1, pp. 40–49, Jan. 2021, doi: 10.1109/MIC.2020.3037034.

[19] S. Riyadi, A. DivayuAndriyani, and S. Noraini Sulaiman, “Improving Hate Speech Detection Using Double-Layers Hybrid CNN-RNN Model on Imbalanced Dataset,” IEEE Access, vol. 12, pp. 159660–159668, 2024, doi: 10.1109/ACCESS.2024.3487433.

[20]S. Khan et al., “BiCHAT: BiLSTM with deep CNN and hierarchical attention for hate speech detection,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 7, pp. 4335–4344, July 2022, doi: 10.1016/j.jksuci.2022.05.006.

[21] A. Baruah, K. Das, and F. Barbhuiya, “Aggression Identification in English, Hindi and Bangla Text using BERT, RoBERTa and SVM,” in Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying, 2020.

[22] R. Ali, U. Farooq, U. Arshad, W. Shahzad, and M. O. Beg, “Hate speech detection on Twitter using transfer learning,” Comput. Speech Lang., vol. 74, p. 101365, July 2022, doi: 10.1016/j.csl.2022.101365.

[23] R. T. Mutanga, N. Naicker, and O. O, “Hate Speech Detection in Twitter using Transformer Methods,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 9, 2020, doi: 10.14569/IJACSA.2020.0110972.

[24] P. He, X. Liu, J. Gao, and W. Chen, "DeBERTa: Decoding-enhanced BERT with Disentangled Attention," in Proc. ICLR 2021. arXiv:2006.03654.

[25] K. Clark, M.-T. Luong, Q. V. Le, and C. D. Manning, "ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators," in Proc. ICLR 2020. arXiv:2003.10555.

[26] T. Caselli, V. Basile, J. Mitrovic, and M. Granitzer, "HateBERT: Retraining BERT for Abusive Language Detection in English," in Proc. 5th Workshop on Online Abuse and Harms (WOAH), ACL 2021, pp. 17–25.

[27] T. Huang, C. Kwak, and F. Chen, "Is LLM-as-a-Judge Robust? Investigating Universal Adversarial Attacks on Zero-shot LLM Assessment," arXiv:2402.14016, 2024.

[28] H. Kiela et al., "The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes," in Proc. NeurIPS 2020, pp. 2611–2624.

[29] T. Davidson, D. Warmsley, M. Macy, and I. Weber, "Automated Hate Speech Detection and the Problem of Offensive Language," in Proc. ICWSM 2017, vol. 11, pp. 512–515.

[30] B. Mathew, P. Saha, S. M. Yimam, C. Biemann, P. Goyal, and A. Mukherjee, "HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection," in Proc. AAAI 2021, pp. 14867–14875.

[31] I. Mollas, Z. Chrysopoulou, S. Karlos, and G. Tsoumakas, "ETHOS: A Multi-Label Hate Speech Detection Dataset," Complex Intell. Syst., vol. 8, pp. 4663–4678, 2022, doi: 10.1007/s40747-021-00608-2.

Keywords: Hate Speech Detection, Natural Language Processing (NLP), Machine Learning, Deep Learning Ensemble

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Special Edition ICAICS-2026 | 1 (March)

Advancement in Automated Hate Speech Detection: A Survey of Machine Learning and Ensemble Methods