TARU PUBLICATIONS
Journal of Information and Optimization Sciences cover

Journal of Information and Optimization Sciences

(Since 1980)
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

Powered by:Powered by

Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
submissions@tarupublications.com
Taru Online First Current IssueSpecial IssuesBrowse all Issues
Journal of Information and Optimization Sciences
Open Access Research Article

Efficient machine learning techniques for hate speech detection in social networks

* ,

* Corresponding author · click or hover a name for details

pp. 2053–2064Vol. 47Issue 5-BMay 2026DOI: 10.47974/JIOS-2296XML
Received:
01 Apr 2025
Published Online:
23 Apr 2026
Article type:
Research Article
Language:
EN
Article no.:
JIOS-2296
Pages:
2053–2064

Abstract

In social networks, hate speech detection is an emerging need for avoiding offensive messages as well as to block the corresponding users. Many users are affected by these messages on social networks like Facebook, Twitter, LinkedIn, etc. Machine learning techniques have progressed significantly in detecting and classifying hate speech. Recently, the long-short term memory (LSTM) with term frequency inverse document frequency (TF-IDF) has been developed for hateful sentiment detection using tweets datasets. The LSTM with TF-IDF efficiently detected hate speech when compared with other successful existing classifier models, support vector machine (SVM), logistic regression (LR), random forest (RF), and bidirectional encoder representations from transformers (BERT) models. Hate speech detection becomes a more challenging issue than hateful sentiments. Thus, this paper focused on effective training of hate speech using either the extraction of TF-IDF or topic-based features. However, TF-IDF-based features become worse due to the occurrence of the scalability problem of large-sized tweet datasets. Thus, the proposed work is framed with two crucial steps to overcome this scalability issue, in which, initially, the topic-based features are extracted using the deep-based models since topic-based features take a smaller number of dimensions than TF-IDF-based features. These features are fused with classifier models to deliver efficient hate speech detection results. In the experimental work, a total of 24,783 labelled tweets were taken as hate, offensive language, or neither, and demonstrated that the proposed deep-based fusion model efficiently detects hate speech better than other classifiers.

Keywords

Hate speech detectionClassifierTopic modelsScalabilityDeep-based fusion models

Subject Classifications

Primary 68U15Secondary 68U35

References

[1] Z. Mossie and J.-H. Wang, “Vulnerable community identification using hate speech detection on social media,” Information Processing & Management, vol. 57, no. 3 (2020).
[2] S. Wachs, M. F. Wright, and M. Gámez-Guadix, “From hate speech to HateLess: The effectiveness of a prevention program on adolescents’ online hate speech involvement,” Computers in Human Behavior, vol. 157 (2024).
[3] Anjum and R. Katarya, “Hate speech, toxicity detection in online social media: A recent survey of state of the art and opportunities,” International Journal of Information Security, vol. 23, no. 1, pp. 577–608 (2024).
[4] G. Kumar, J. P. Singh, and A. K. Singh, “Autoencoder-based feature extraction for identifying hate speech spreaders in social media,” IEEE Transactions on Computational Social Systems, vol. 11, no. 4, pp. 4819–4827 (2024).
[5] 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).
[6] A. Toktarova, D. Syrlybay, B. Myrzakhmetova, G. Anuarbekova, G. Rakhimbayeva, B. Zhylanbaeva, N. Suieuova, and M. Kerimbekov, “Hate speech detection in social networks using machine learning and deep learning methods,” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 5 (2023), doi:10.14569/IJACSA.2023.0140542.
[7] J. S. Malik, H. Qiao, G. Pang, and A. van den Hengel, “Deep learning for hate speech detection: A comparative study,” International Journal of Data Science and Analytics, vol. 20, pp. 3053–3068 (2025).
[8] P. Badjatiya, S. Gupta, M. Gupta, and V. Varma, “Deep learning for hate speech detection in tweets,” in Proc. 26th Int. Conf. World Wide Web Companion, pp. 759–760 (2017).
[9] M. Athoillah and R. K. Putri, “Utilizing support vector machines to detect hate speech on social media,” Science, Engineering and Technology, vol. 4, no. 2, pp. 53–60 (2024).
[10] P. S. Br Ginting, B. Irawan, and C. Setianingsih, “Hate speech detection on Twitter using multinomial logistic regression classification method,” in Proc. IEEE Int. Conf. Internet of Things and Intelligence System (IoTaIS), Bali, Indonesia (2019).
[11] C. Mallick, S. Mishra, S. Satpathy, and M. M. V. Chalapathi, “A gated recurrent unit-based model for fake news detection in online social networks,” Journal of Information and Optimization Sciences, vol. 46, no. 6, pp. 1841–1850 (2025), doi : 10.47974/JIOS-2013.
[12] A. C. Mazari, N. Boudoukhani, and A. Djeffal, “BERT-based ensemble learning for multi-aspect hate speech detection,” Cluster Computing, vol. 27, no. 1, pp. 325–339 (2024).
[13] N. Teja Sri, K. Geethika, N. Kotha, and H. Kandoori, “Modified TF-IDF with machine learning classifier for hate speech detection on Twitter,” Turkish Journal of Computer and Mathematics Education, vol. 14, no. 3, pp. 978–984 (2023).
[14] M. S. Basha, S. K. Mouleeswaran, and K. R. Prasad, “Sampling-based visual assessment computing techniques for an efficient social data clustering,” Journal of Supercomputing, vol. 77, pp. 8013–8037 (2021), doi: 10.1007/s11227-021-03618-6.
[15] P. K. Mannepalli, S. Sharma, N. Rajput, and S. Kalamdhad, “A comprehensive analysis of hate speech detection and recognition utilising advanced machine learning algorithms and techniques,” in Proc. Int. Conf. Automation and Computation (AUTOCOM), Dehradun, India (2025).
[16] H. Mehta and K. Passi, “Social media hate speech detection using explainable artificial intelligence (XAI),” Algorithms, vol. 15, no. 8 (2022).
[17] A. Annamalai, R. C. Poonia, and S. Shanmugasundaram, “Internet of things-based virtual private social networks on a text messaging strategy on mobile platforms,” International Journal of Electronic Security and Digital Forensics, vol. 16, no. 1, pp. 40–62 (2024).
[18] V. U. Gongane, M. V. Munot, and A. D. Anuse, “A survey of explainable AI techniques for detection of fake news and hate speech on social media platforms,” Journal of Computational Social Science, vol. 7, no. 1, pp. 587–623 (2024).
[19] A. Chhabra and D. K. Vishwakarma, “Hate content detection via novel pre-processing sequencing and ensemble methods,” arXiv, vol. 2409, no. 05134 (2024).
[20] K. R. Prasad and M. S. Basha, “Improving the performance of speech clustering method,” in Proc. 10th Int. Conf. Intelligent Systems and Control (ISCO), Coimbatore, India, pp. 1–5 (2016), doi: 10.1109/ISCO.2016.7726878.
[21] A. Shawon, S. T. Zuhori, F. Mahmud, and M. J.-U. Rahman, “Website classification using word-based multiple N-gram models and random search oriented feature parameters,” in Proc. 21st Int. Conf. Computer and Information Technology (ICCIT), Dhaka, Bangladesh (2018).
[22] E. Dara, B. Unhelkar, S. S. Shankar, T. Chakrabarti, P. Chakrabarti, B. Sivaneasan, and M. Margala, “Improving imbalanced data classification in healthcare systems: A novel optimization approach with statistical insights,” Journal of Information and Optimization Sciences, vol. 45, no. 8, pp. 2203–2212 (2024), doi: 10.47974/JIOS-1783.
[23] G. Behl, H. Padalikar, S. Vijaykumar, P. Pujari, and B. Kaur, “Optimizing agile adoption in virtual learning environments: A framework for classification and prioritization,” Journal of Information and Optimization Sciences, vol. 45, no. 6, pp. 1705–1716 (2024), doi: 10.47974/JIOS-1706.
[24] S. G. Kale, S. N. Gambhire, D. Deshmukh, D. Dhabliya, Y. Bangera, and D. J. Dahigaonkar, “Leveraging machine learning for real-time optimization in large-scale information systems,” Journal of Information and Optimization Sciences, vol. 46, no. 4-B, pp. 1289–1301 (2025), doi: 10.47974/JIOS-1990.
[25] M. Adlakha, M. A. Alam, S. Naaz, and S. Zafar, “Integrating natural therapeutics in machine learning models for diabetes mellitus optimization,” Journal of Information and Optimization Sciences, vol. 47, no. 1, pp. 371–398 (2026), doi: 10.47974/JIOS-2182.

Views: 103Downloads: 67Citations: 0