## 6 Conclusions and Future Work
**Institutional Review Board Statement:** Not applicable  
**Informed Consent Statement:** Not applicable  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request. **Acknowledgments:** This work was supported by Researchers Supporting Project No. (RSPD2024R1062), King Saud University, Riyadh, Saudi Arabia. The authors are also grateful to the Ministry of Education, India, and the Indian Institute of Information Technology, Allahabad, for providing the necessary materials to complete this work. **Conflicts of Interest:** The authors declare no conflicts of interest. The authors unequivocally state that they have no identifiable financial conflicts of interest or personal affiliations that could have reasonably affected the integrity of the research as presented in this paper. * [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON] Automated hate speech detection and the problem of offensive language. In Proceedings of the International AAAI Conference on Web and Social Media, Montreal, QC, Canada, 15-18 May 2017; Volume 11, pp. 512-515. * [PERSON] et al. (2016) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Abusive language detection in online user content. In Proceedings of the 25 th International Conference on World Wide Web, Montreal, QC, Canada, 11-15 May 2016; pp. 145-153. * [PERSON] et al. (2019) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Tackling online abuse: A survey of automated abuse detection methods. _arXiv_ **2019**, arXiv:1908.06024. * [PERSON] and [PERSON] (2017) [PERSON]; [PERSON] [PERSON] A survey on hate speech detection using natural language processing. In Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media, Valencia, Spain, 3-4 April 2017; pp. 1-10. * [PERSON] and [PERSON] (2019) [PERSON]; [PERSON] [PERSON] Detection of hate speech in social networks: A survey on multilingual corpus. In Proceedings of the 6 th International Conference on Computer Science and Information Technology, Dubai, United Arab Emirates, 23-24 February 2019; Volume 10, pp. 83-100. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Resources and benchmark corpora for hate speech detection: A systematic review. _Lang. Resour. Eval._**2021**, _55_, 477-523. [CrossRef]
* [PERSON] and [PERSON] (2019) [PERSON] [PERSON]; [PERSON] Cyberbullies in Twitter: A focused review. In Proceedings of the 2019 14 th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP), Larnaca, Cyprus, 9-10 June 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 1-6. * [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] Harmful Language Datasets: An Assessment of Robustness. In Proceedings of the 7 th Workshop on Online Abuse and Harms (WOAH), Toronto, ON, Canada, 13 July 2023; pp. 221-230. * [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON] A review on offensive language detection. _Adv. Data Inf. Sci._**2020**, _94_, 433-439. * [PERSON] and [PERSON] (2020) [PERSON]; [PERSON] Detecting weak and strong Islamophobic hate speech on social media. _J. Inf. Technol. Politics_**2020**, _17_, 66-78. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON] Online hate speech against women: Automatic identification of misogyny and sexism on twitter. _J. Intell. Fuzzy Syst._**2019**, _36_, 4743-4752. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON] [PERSON] Overview of the task on automatic misogyny identification at IberEval 2018. _Ibereval@ Sephn_**2018**, _2150_, 214-228. * [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON] [PERSON] AMI@ EVALITA2020: Automatic misogyny identification. In Proceedings of the 7 th Evaluation Campaign of Natural Language Processing and Speech tools for Italian (EVALITA 2020), Online, 17 December 2020. * [PERSON] et al. (2016) [PERSON]; [PERSON] Hateful symbols or hateful people?