DKNN and HPFS: An Efficacious Deep Learning Approach with Fuzzy Sets for Social Network Hostility

Document Type : Research Paper

Authors

Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Deemed to be University, Vaddeswaram, Andhra Pradesh, 522302, India.

Abstract

Hate speech and hateful language have become more accessible to spread as a result of the increase in social media and digital contacts. Cyberbullying is the term used to describe these kinds of online insults, attacks, and harassment. Regretfully, the prevalence of cyberbullying has increased, with those who engage in it hiding behind an illusion of relative online anonymity. Finding such offensive content has become difficult due to the overwhelming amount of user-generated content. Text categorization is a broad field of machine learning. Because deep learning techniques outperform typical machine learning algorithms in various ways, researchers are turning to them to detect cyberbullying. This research proposes a new deep learning (DL)--based technique to overcome the issues of cyberbullying content recognition. To detect and classify the bullying content from pre-processed data using selected essential features, the Deep Kronecker Neural Network (DKNN) technique was employed. Comparing different classification strategies with the proposed approach, the extensive tests conducted on the two datasets demonstrate the significance of this work. We provide a novel technique for cyberbullying detection: the DKNN technique outperforms existing state-of-the-art methods with up to 99.56% accuracy results.

Keywords

Main Subjects

References

[1] T. Ahmed, S. Ivan, M. Kabir, H. Mahmud, K. Hasan, Performance analysis of transformer-based architectures
and their ensembles to detect trait-based cyberbullying, Social Network Analysis and Mining, 12(1) (2022), 99.
https://doi.org/10.1007/s13278-022-00934-4
[2] A. Akhter, U. K. Acharjee, M. A. Talukder, M. M. Islam, M. A. Uddin, A robust hybrid machine learning model
for detecting Bengali cyberbullying in social media, Natural Language Processing Journal, 4 (2023), 100027. https:
//doi.org/10.1016/j.nlp.2023.100027
[3] A. Al-Marghilani, Artificial intelligence-enabled cyberbullying-free online social networks in smart cities, International
Journal of Computational Intelligence Systems, 15(1) (2022), 9. https://doi.org/10.1007/
s44196-022-00063-y
[4] A. Almomani, K. Nahar, M. Alauthman, M. A. Al-Betar, Q. Yaseen, B. B. Gupta, Image cyberbullying detection
and recognition using transfer deep machine learning, International Journal of Cognitive Computing in Engineering,
5 (2024), 14-26. https://doi.org/10.1016/j.ijcce.2023.11.002
[5] A. F. Alqahtani, M. Ilyas, An ensemble-based multi-classification machine learning classifiers approach to detect
multiple classes of cyberbullying, Machine Learning and Knowledge Extraction, 6(1) (2024), 156-170. https://doi.
org/10.3390/make6010009
[6] M. Alzaqebah, G. M. Jaradat, D. Nassan, R. Alnasser, M. K. Alsmadi, I. Almarashdeh, S. Alkhushayni, Cyberbullying
detection framework for short and imbalanced Arabic datasets, Journal of King Saud University-Computer and
Information Sciences, 35(8) (2023), 101652. https://doi.org/10.1016/j.jksuci.2023.101652
[7] K. I. Arce-Ruelas, O. Alvarez-Xochihua, L. Pellegrin, L. Cardoza-Avenda˜no, J. ´A. Gonz´alez-Fraga, Automatic cyberbullying detection: A Mexican case in high school and higher education students, IEEE Latin America Transactions,
20(5) (2022), 770-779. https://doi.org/10.1109/TLA.2022.9693561
[8] C. E. Gomez, M. O. Sztainberg, R. E. Trana, Curating cyberbullying datasets: A human-AI collaborative approach,
International Journal of Bullying Prevention, 4(1) (2022), 35-46. https://doi.org/10.1007/s42380-021-00114-6
[9] C. Iwendi, G. Srivastava, S. Khan, P. K. R. Maddikunta, Cyberbullying detection solutions based on deep learning
architectures, Multimedia Systems, 29(3) (2023), 1839-1852. https://doi.org/10.1007/s00530-020-00701-5
[10] A. Kumar, N. Sachdeva, A Bi-GRU with attention and CapsNet hybrid model for cyberbullying detection on social
media, World Wide Web, 25(4) (2022), 1537-1550. https://doi.org/10.1007/s11280-021-00920-4
[11] A. Kumar, N. Sachdeva, Multi-input integrative learning using deep neural networks and transfer learning for
cyberbullying detection in real-time code-mix data, Multimedia Systems, 28(6) (2022), 2027-2041. https://doi.
org/10.1007/s00530-020-00672-7
[12] A. Kumar, N. Sachdeva, Multimodal cyberbullying detection using capsule network with dynamic routing and
deep convolutional neural network, Multimedia Systems, 28(6) (2022), 2043-2052. https://doi.org/10.1007/
s00530-020-00747-5
[13] K. Maity, A. Kumar, S. Saha, A multitask multimodal framework for sentiment and emotion-aided cyberbullying
detection, IEEE Internet Computing, 26(4) (2022), 68-78. https://doi.org/10.1109/MIC.2022.3158583
[14] A. Mangaonkar, R. Pawar, N. S. Chowdhury, R. R. Raje, Enhancing collaborative detection of cyberbullying
behaviour in Twitter data, Cluster Computing, 25(2) (2022), 1263-1277. https://doi.org/10.1007/
s10586-021-03483-1
[15] B. A. H. Murshed, J. Abawajy, S. Mallappa, M. A. N. Saif, H. D. E. Al-Ariki, DEA-RNN: A hybrid deep learning
approach for cyberbullying detection in Twitter social media platform, IEEE Access, 10 (2022), 25857-25871. https:
//doi.org/10.1109/ACCESS.2022.3153675
[16] B. A. H. Murshed, Suresha, J. Abawajy, M. A. N. Saif, H. M. Abdulwahab, F. A. Ghanem, FAEO-ECNN:
Cyberbullying detection in social media platforms using topic modelling and deep learning, Multimedia Tools and
Applications, 82(30) (2023), 46611-46650. https://doi.org/10.1007/s11042-023-15372-3
[17] V. L. Paruchuri, P. Rajesh, CyberNet: A hybrid deep CNN with N-gram feature selection for cyberbullying detection
in online social networks, Evolutionary Intelligence, 16(6) (2023), 1935-1949. https://doi.org/10.1007/
s12065-022-00774-3
[18] S. Paul, S. Saha, CyberBERT: BERT for cyberbullying identification: BERT for cyberbullying identification, Multimedia
Systems, 28(6) (2022), 1897-1904. https://doi.org/10.1007/s00530-020-00710-4
[19] S. Paul, S. Saha, M. Hasanuzzaman, Identification of cyberbullying: A deep learning based multimodal approach,
Multimedia Tools and Applications, 1 (2022), 1-20. https://doi.org/10.1007/s11042-020-09631-w
[20] P. K. Roy, F. U. Mali, Cyberbullying detection using deep transfer learning, Complex and Intelligent Systems, 8(6)
(2022), 5449-5467. https://doi.org/10.1007/s40747-022-00772-z
[21] R. Suhas Bharadwaj, S. Kuzhalvaimozhi, N. Vedavathi, A novel multimodal hybrid classifier-based cyberbullying
detection for social media platforms, In Proceedings of the Computational Methods in Systems and Software, 1(2022), 689-699. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-21438-7_57
[22] F. Wu, B. Gao, X. Pan, Z. Su, Y. Ji, S. Liu, Z. Liu, FACapsnet: A fusion capsule network with congruent attention
for cyberbullying detection, Neurocomputing, 542 (2023), 126253. https://doi.org/10.1016/j.neucom.2023.
126253
Iranian Journal of Fuzzy Systems
Volume 21, Issue 4
July and August 2024
Pages 141-162

Files

Share

How to cite

Statistics