Enhanced Seagull Optimization with Natural Language Processing Based Hate Speech Detection and Classification
Source abstract
Abstract
Hate speech has become a hot research topic in the area of natural language processing (NLP) due to the tremendous increase in the usage of social media platforms like Instagram, Twitter, Facebook, etc. The facelessness and flexibility provided through the Internet have made it easier for people to interact aggressively. Furthermore, the massive quantity of increasing hate speech on social media with heterogeneous sources makes it a challenging task. With this motivation, this study presents an Enhanced Seagull Optimization with Natural Language Processing Based Hate Speech Detection and Classification (ESGONLP-HSC) model. The major intention of the presented ESGONLP-HSC model is to identify and classify the occurrence of hate speech on social media websites. To accomplish this, the presented ESGONLP-HSC model involves data pre-processing at several stages, such as tokenization, vectorization, etc. Additionally, the Glove technique is applied for the feature extraction process. In addition, an attention-based bidirectional long short-term memory (ABLSTM) model is utilized for the classification of social media text into three classes such as neutral, offensive, and hate language. Moreover, the ESGO algorithm is utilized as a hyperparameter optimizer to adjust the hyperparameters related to the ABLSTM model, which shows the novelty of the work. The experimental validation of the ESGONLP-HSC model is carried out, and the results are examined under diverse aspects. The experimentation outcomes reported the promising performance of the ESGONLP-HSC model over recent state of art approaches.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Hate Speech and Cyberbullying Detection · Spam and Phishing Detection · Advanced Malware Detection Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
Department of Computer Science, College of Computing Al Qunfidhah, Umm Al-Qura University, Mecca 24382, Saudi Arabia
Department of Computer Science, College of Science and Humanities, Prince Sattam Bin AbdulAziz University, Slayel 11913, Saudi Arabia
Department of Mathematics, Faculty of Science, Sohag University, Sohag 82524, Egypt
Department of Mathematics, College of Science, Taif University, Taif 21944, Saudi Arabia
Department of Mathematics, Faculty of Science, New Valley University, El-Kharga 72511, Egypt
Funding
Deanship of Scientific Research at Umm Al-Qura University · Deanship of Scientific Research at Najran University for funding this work under the Research Collaboration Funding program
References (30)
Show source-provided references
1. García-Díaz, J.A., Jiménez-Zafra, S.M., García-Cumbreras, M.A., and Valencia-García, R. (2022). Evaluating feature combination strategies for hate-speech detection in spanish using linguistic features and transformers. Complex Intell. Syst., 1–22.10.1007/s40747-022-00693-x ↗
2. Challenges of hate speech detection in social media10.1007/s42979-021-00457-3 ↗
3. Jahan, M.S., and Oussalah, M. (2021). A systematic review of Hate Speech automatic detection using Natural Language Processing. arXiv.
4. Alkomah, F., and Ma, X. (2022). A Literature Review of Textual Hate Speech Detection Methods and Datasets. Information, 13.10.14569/IJACSA.2022.01308100 ↗
5. Automatic hate speech detection using killer natural language processing optimizing ensemble deep learning approach10.1007/s00607-019-00745-0 ↗
6. Pariyani, B., Shah, K., Shah, M., Vyas, T., and Degadwala, S. (2021, January 4–6). February. Hate speech detection in twitter using natural language processing. Proceedings of the 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), Tirunelveli, India.10.1109/ICICV50876.2021.9388496 ↗
7. Perifanos, K., and Goutsos, D. (2021). Multimodal Hate Speech Detection in Greek Social Media. Multimodal Technol. Interact., 5.10.3390/mti5070034 ↗
8. Comparing pre-trained language models for Spanish hate speech detection10.1016/j.eswa.2020.114120 ↗
9. HCovBi-caps: Hate speech detection using convolutional and Bi-directional gated recurrent unit with Capsule network10.1109/ACCESS.2022.3143799 ↗
10. BiCHAT: BiLSTM with deep CNN and hierarchical attention for hate speech detection
11. Investigating the Effect of Preprocessing Arabic Text on Offensive Language and Hate Speech Detection
12. Pham, Q.H., Nguyen, V.A., Doan, L.B., Tran, N.N., and Thanh, T.M. (2020, January 12–14). November. From universal language model to downstream task: Improving RoBERTa-based Vietnamese hate speech detection. Proceedings of the 2020 12th International Conference on Knowledge and Systems Engineering (KSE), Can Tho, Vietnam.10.1109/KSE50997.2020.9287406 ↗
13. Robinson, D., Zhang, Z., and Tepper, J. (2018, January 3–7). Hate speech detection on twitter: Feature engineering vs feature selection. Proceedings of the European Semantic Web Conference, Crete, Greece.10.1007/978-3-319-98192-5_9 ↗
14. Awal, M.R., Cao, R., Lee, R.K.W., and Mitrović, S. (2021, January 11–14). Angrybert: Joint learning target and emotion for hate speech detection. Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, Virtual Event.10.1007/978-3-030-75762-5_55 ↗
15. Un-compromised credibility: Social media based multi-class hate speech classification for text10.1109/ACCESS.2021.3101977 ↗
16. Mohtaj, S., Schmitt, V., and Möller, S. (2022). A Feature Extraction based Model for Hate Speech Identification. arXiv.
17. Kumar, D., Kumar, N., and Mishra, S. (2021, January 17–20). QUARC: Quaternion multi-modal fusion architecture for hate speech classification. Proceedings of the 2021 IEEE International Conference on Big Data and Smart Computing (BigComp), Jeju Island, Korea.10.1109/BigComp51126.2021.00075 ↗
18. An ensemble method for radicalization and hate speech detection online empowered by sentic computing10.1007/s12559-021-09845-6 ↗
19. To BAN or not to BAN: Bayesian attention networks for reliable hate speech detection10.1007/s12559-021-09826-9 ↗
20. Unintended bias evaluation: An analysis of hate speech detection and gender bias mitigation on social media using ensemble learning10.1016/j.eswa.2022.117032 ↗
21. Cruz, R.M., de Sousa, W.V., and Cavalcanti, G.D. (2022). Selecting and combining complementary feature representations and classifiers for hate speech detection. arXiv.10.1016/j.osnem.2021.100194 ↗
22. Yao, Z., Sun, Y., Ding, W., Rao, N., and Xiong, H. Dynamic word embeddings for evolving semantic discovery. Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, Marina Del Rey, CA, USA.
23. Hong, G. (2005, January 11-13). Relation extraction using support vector machine. Proceedings of the International Conference on Nat-Ural Language Processing, Jeju Island, Korea.10.1007/11562214_33 ↗
24. Zhang, Z., Robinson, D., and Tepper, J. (2018, January 3–7). Detecting hate speech on Twitter using a convolutionGRU based deep neural network. Proceedings of the European Semantic Web Conference, Crete, Greece.10.1007/978-3-319-93417-4_48 ↗
25. Kim, Y., Jernite, Y., Sontag, D., and Rush, A.M. (2016, January 12–17). Character-aware neural language models. Proceedings of the Thirtieth AAAI Conference on Artifcial Intelligence, Phoenix, AZ, USA.10.1609/aaai.v30i1.10362 ↗
26. Pennington, J., Socher, R., and Manning, C.D. (2014, January 25–29). Glove: Global vectors for word representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar.10.3115/v1/D14-1162 ↗
27. Bidirectional LSTM with attention mechanism and convolutional layer for text classification10.1016/j.neucom.2019.01.078 ↗
28. Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems10.1016/j.knosys.2018.11.024 ↗
29. Earl, J., and Rohlinger, D.A. (2012). A social movement online community: Stormfront and the white nationalist movement. Media, Movements, and Political Change (Research in Social Movements, Conficts and Change, Volume 33), Emerald Group Publishing Limited.
30. Davidson, T., Warmsley, D., Macy, M., and Weber, I. (2017, January 15–18). Automated Hate Speech Detection and the Problem of Offensive Language. Proceedings of the 11th International Conference on Web and Social Media (ICWSM), Montreal, QC, Canada.10.1609/icwsm.v11i1.14955 ↗