Skip to main content
SpringerLink
Log in

NDDSM: Novel Deep Decision-Support Model for Hate Speech Detection

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

The pervasiveness of social media in people's lives is indisputable, the issue where it has become a necessary part of daily practices. However, unrestricted access to social media allows anonymous individuals to spread meaningless or even hostile information, defeating communication's purpose. Social media’s positive and negative impact on society or individuals becomes more pronounced as its usage increases. As the harmful effect of unmonitored ‘hate speech’ becomes increasingly apparent, detecting such content has become a crucial concern in social media. In a recent study, machine-learning models have been developed to identify hate speech across multiple languages. As a result, the use of Bidirectional long short-term memory (Bi-LSTM) and convolutional neural network (CNN) for feature extraction in evaluating and identifying hate speech has risen. However, LSTM and CNN hyperparameters are typically selected based on expert opinion and prior research, making it difficult for the model to generalize since its creators need to know the optimal values for its parameters. To address this issue, we propose a novel deep decision support model which uses the sparrow search algorithm (SSA) to optimize the Bi-LSTM and CNN model hyperparameters for detecting hate speech. We employed the SSA for the decision support system to identify the best hyperparameters for the model architecture to improve its interpretability and accuracy. The benchmark datasets have been used to evaluate the model's performance, and the results indicate that our proposed model outperforms conventional hate speech detection systems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Algorithm 1
Algorithm 2
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Mandl, T. et al., “Overview of the hasoc track at fire 2019: Hate speech and offensive content identification in Indo-European languages”. In Proceedings of the 11th forum for information retrieval evaluation, pp. 14–17, 2019.

  2. Zampieri, M. et al.,”Predicting the type and target of offensive posts in social media, in: NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies – Proceedings of the Conference. Association for Computational Linguistics (ACL), pp. 1415–1420,2019.

  3. Visualizing Eight Years of Twitter’s Evolution: 2012–2019. 14 November 2019. Available online: https://blog.gdeltproject.org/visualizing-eight-years-of-twitters-evolution2012-2019/ (accessed on 12 February 2022).

  4. Hochreiter S, Schmidhuber J. LSTM can solve hard long time lag problems. Advances in neural information processing systems. 1996.

  5. Davidson, T. et al., “Automated hate speech detection and the problem of offensive language”, in Proceedings of the 11th International Conference on Web and social media, ICWSM 2017. AAAI Press, pp. 512–515,2017.

  6. Ousidhoum, N. et al.” Multilingual and multi-aspect hate speech analysis”. arXiv preprint arXiv:1908.11049,2019.

  7. Founta, A. M., Chatzakou, D., Kourtellis, N., Blackburn, J., Vakali, A., & Leontiadis, I. (2018). A Unified Deep Learning Architecture for Abuse Detection. WebSci 2019 - Proceedings of the 11th ACM Conference on Web Science, 105–114. https://doi.org/10.48550/arxiv.1802.00385

  8. Arras, L., Montavon, G., Müller, K. R., Samek, W. (2017). Explaining recurrent neural network predictions in sentiment analysis. EMNLP 2017 - 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 743 2017 - Proceedings of the Workshop. https://doi.org/10.18653/v1/w17-5221.

  9. O. Kanerva, “Evaluating explainable AI models for convolutional neural networks with proxy tasks,”2019,https://www.semanticscholar.org/paper/Evaluating-explainable-AI-models-for-convolutional_Kanerva/d91062a3e13ee034af6807e1819a9ca3051daf13.

  10. Isnain, Auliya Rahman, Agus Sihabuddin, and Yohanes Suyanto. "Bidirectional long short-term memory method and Word2vec extraction approach for hate speech detection." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 14.2, pp. 169–178 ,2020.

  11. Zhou Y, Yang Y, Liu H, Liu X, Savage N. Deep learning-based fusion approach for hate speech detection. IEEE Access,128923–9, 2020.

  12. Ganfure GO. Comparative analysis of deep learning based Afaan Oromo hate speech detection. Journal of Big Data, 2022.

  13. A. Ebrahimi Fard, M. Mohammadi, Y. Chen, and B. Van de Walle, “Computational rumor detection without non-rumor: A one-class classification approach,” IEEE Transactions on Computational Social Systems, vol. 6, no. 5, pp. 830–846, 2019.

  14. Zhang, Z. et al., “Hate Speech Detection Using a Convolution-LSTM Based Deep Neural Network”. European Semantic Web Conference, pp.745–760, 2018.

  15. Pennington, J. et al., “GloVe: Global vectors for word representation”, in: EMNLP 2014 – 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference.

  16. Waseem, Zeerak, and Dirk Hovy. "Hateful symbols or hateful people? predictive features for hate speech detection on twitter." In Proceedings of the NAACL student research workshop, pp. 88–93. 2016.

  17. ElSherief M, et al. Peer to peer hate: hate speech instigators and their targets. In: Proceedings of the twelfth international AAAI conference on web and social media, Palo Alto; 2018.

  18. Malmasi S, Zampieri M. Challenges in discriminating profanity from hate speech. J Exp Theor Artif Intell. 2018;30:187–202.

    Article  Google Scholar 

  19. Al-Ajlan, M.A.; Ykhlef, M. Optimized twitter cyberbullying detection based on deep learning. In Proceedings of the 2018 21st Saudi Computer Society National Computer Conference (NCC), Riyadh, Saudi Arabia, 25–26 April 2018; pp.1–5.

  20. Ahmed, M.T.; Rahman, M.; Nur, S.; Islam, A.; Das, D. Deployment of Machine Learning and Deep Learning Algorithms in Detecting Cyberbullying in Bangla and Romanized Bangla text: A Comparative Study. In Proceedings of the 2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), Bhilai, India, 19–20 February 2021.

  21. Dadvar, M.; Eckert, K. Cyberbullying detection in social networks using deep learning-based models. In International Conference on Big Data Analytics and Knowledge Discovery; Springer: Cham, Switzerland, 2020.

  22. Luo X. Efficient English text classification using selected machine learning techniques. Alex Eng J. 2021;60:3401–9.

    Article  Google Scholar 

  23. Khan U, Khan S, Rizwan A, Atteia G, Jamjoom MM, Samee NA. Aggression detection in social media from textual data using deep learning models. Appl Sci. 2022;12:5083.

    Article  Google Scholar 

  24. Himdi H, Weir G, Assiri F, et al. Arabic fake news detection based on textual analysis. Arab J Sci Eng. 2022;47:10453–69. https://doi.org/10.1007/s13369-021-06449-y.

    Article  Google Scholar 

  25. Alorainy W, Burnap P, Liu H, Williams ML. “the enemy among us”: Detecting cyber hate speech with threats-based othering language embeddings. ACM Trans Web. 2019;13(3):1–26.

    Article  Google Scholar 

  26. Sequeira R, Gayen A, Ganguly N, Dandapat SK, Chandra J. A large-scale study of the twitter follower network to characterize the spread of prescription drug abuse tweets. IEEE Trans Comput Social Syst. 2019;6(6):1232–44.

    Article  Google Scholar 

  27. Zhao R, Mao K. Cyberbullying detection based on semantic-enhanced marginalized denoising auto-encoder. IEEE Trans Affect Comput. 2017;8(3):328–39.

    Article  Google Scholar 

  28. Wang J, Yu L, Lai KR, Zhang X. Tree-structured regional cnn-lstm model for dimensional sentiment analysis. IEEE/ACM Trans Audio, Speech, Lang Process. 2020;28:581–91.

    Article  Google Scholar 

  29. Jiankai X, Bo S. A novel swarm intelligence optimization approach: sparrow search algorithm. Syst Scie Control Eng. 2020;8(1):22–34.

    Article  Google Scholar 

  30. Freitas D, Lopes LG, Morgado-Dias F. Particle swarm optimisation: a historical review up to the current developments. Entropy. 2020;22:362.

    Article  MathSciNet  Google Scholar 

  31. Liu F, Qin P, You J, Fu Y. Sparrow search algorithm-optimized long short-term memory model for stock trend prediction. Comput Intellig Neurosci. 2022;12(2022):3680419. https://doi.org/10.1155/2022/3680419.PMID:35990139;PMCID:PMC9391098.

    Article  Google Scholar 

  32. Rajathi GI, Kumar RR, Ravikumar D, Joel T, Kadry S, et al. Brain tumor diagnosis using sparrow search algorithm based deep learning model. Comput Syst Sci Eng (CSSE). 2023;44(2):1793–806.

    Article  Google Scholar 

  33. C. L. Zhang and S. F. Ding, “A stochastic configuration network based on chaotic sparrow search algorithm,” Knowledge-Based Systems, vol. 220, Article ID 106924, 2021.

  34. Golbeck, J. et al., “A large, labelled corpus for online harassment research”. In Proceedings of the 2017 ACM on web science conference, pp.229–233,2017.

  35. Fortuna, P. et al.,”A survey on automatic detection of hate speech in text”. ACM Computing Surveys,2018

  36. Chung, Y.L. et al., “CONAN-Counter Narratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech”. arXiv preprint arXiv:1910.03270,2019.

  37. Salminen, J. et al.,” Developing an online hate classifier for multiple social media platforms. Human-centric Computing and Information Sciences,2020.

  38. Kennedy B, et al. “Introducing the Gab Hate Corpus: defining and applying hate-based rhetoric to social media posts at scale. Lang Resour Eval. 2022;56(1):79–108.

    Article  Google Scholar 

  39. Kurrek, J. et al., “Towards a comprehensive taxonomy and large-scale annotated corpus for online slur usage”. In Proceedings of the Fourth Workshop on Online Abuse and Harms, pp. 138–149,2020.

  40. Gambäck, Björn, and Utpal Kumar Sikdar. "Using convolutional neural networks to classify hate-speech." In Proceedings of the first workshop on abusive language online, pp. 85–90. 2017.

  41. Basak R, Sural S, Ganguly N, Ghosh SK. Online public shaming on twitter: detection, analysis, and mitigation. IEEE Trans Comput Social Syst. 2019;6(2):208–20.

    Article  Google Scholar 

  42. Vashistha, Neeraj, and Arkaitz Zubiaga. "Online multilingual hate speech detection: experimenting with Hindi and English social media." Information 12.1, 2020.

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, India

    Ashwini Kumar & Santosh Kumar

Authors
  1. Ashwini Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Santosh Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ashwini Kumar.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Advanced Computing and Data Sciences” guest edited by Mayank Singh, Vipin Tyagi and P.K. Gupta.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, A., Kumar, S. NDDSM: Novel Deep Decision-Support Model for Hate Speech Detection. SN COMPUT. SCI. 5, 67 (2024). https://doi.org/10.1007/s42979-023-02382-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-023-02382-z

Keywords

Search

Navigation