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Reseach Article

Unsupervised Hybrid approaches for Cyberbullying Detection in Instagram

by Abishak I., Kabilash M., Ramesh R., Sheeba J.I., Pradeep Devaneyan S.
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 174 - Number 26
Year of Publication: 2021
Authors: Abishak I., Kabilash M., Ramesh R., Sheeba J.I., Pradeep Devaneyan S.

Abishak I., Kabilash M., Ramesh R., Sheeba J.I., Pradeep Devaneyan S. . Unsupervised Hybrid approaches for Cyberbullying Detection in Instagram. International Journal of Computer Applications. 174, 26 ( Mar 2021), 40-46. DOI=10.5120/ijca2021921191

@article{ 10.5120/ijca2021921191,
author = { Abishak I., Kabilash M., Ramesh R., Sheeba J.I., Pradeep Devaneyan S. },
title = { Unsupervised Hybrid approaches for Cyberbullying Detection in Instagram },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2021 },
volume = { 174 },
number = { 26 },
month = { Mar },
year = { 2021 },
issn = { 0975-8887 },
pages = { 40-46 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2021921191 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T00:23:11.715226+05:30
%A Abishak I.
%A Kabilash M.
%A Ramesh R.
%A Sheeba J.I.
%A Pradeep Devaneyan S.
%T Unsupervised Hybrid approaches for Cyberbullying Detection in Instagram
%J International Journal of Computer Applications
%@ 0975-8887
%V 174
%N 26
%P 40-46
%D 2021
%I Foundation of Computer Science (FCS), NY, USA

In today’s digital society, cyberbullying is serious and widespread issues affecting high number of Internet users, mostly teenager. However, increases in social media usage there is increase in the rise of cyberbullying. Cyberbullying is aggressive act carried out by some person using electronic forms of contact, repeatedly against people who cannot defend themselves. In the existing work distinguished the bullies from normal Instagram users by considering text, network and user related attributes using classifiers. Most existing cyberbullying detection methods are supervised and, thus, have mainly two key drawbacks such as labeling the data often take more time and labor and Current guidelines for labeling may not useful for future instances because of evolving social networks and different language usage. To address these limitations, this proposed work introduces for unsupervised cyberbullying detection method. The proposed detection method will be extract linguistic attributes such as idioms, sarcasm, irony and active or passive voice. In addition, a representation learning network that learns the multi-modal session representations and a multitask learning network will simultaneously estimate bullying energy and then models the comments arriving times from Instagram data set.

  1. P. K. Smith, J. Mahdavi, M. Carvalho, and N. Tippett, “An investigation into cyberbullying, its forms, awareness and impact, and the relationship between age and gender in cyberbullying,”, Research Brief No. RBX03- 06. London: DfES, 2006.
  2. Smith, P. K., Mahdavi, J., Carvalho, M., Fisher, S., Russell, S., & Tippett, N. (2008). “Cyberbullying: its nature and impact in secondary school pupils. Journal of Child Psychology and Psychiatry, 49(4), 376–385.
  3. Athena vakali and Gianluca stringhini 2019, “Detecting cyberbullying and cyberaggression in Social media”, ACM transaction on web vol. 13, no.13 Article 17.
  4. Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy, “Hierarchical attention networks for document classification”, Proceedings of NAACL-HLT 2016, pages 1480–1489.
  5. Thomas N Kipf and Max Welling. “Variational graph auto-encoders”. arXiv preprint arXiv:1611.07308 (2016).
  6. Nargess Tahmasbi and Elham Rastegari, “A Socio-Contextual Approach in Automated Detection of Public Cyberbullying on Twitter”, ACM Transactions on Social Computing, Volume: 1, No. 4, Article 15, 22 pages, December 2018.
  7. Xi tong Yang and Jiebo Luo, “Tracking illicit drug dealing and abuse in Instagram using multi modal analysis”, ACM Transactions on Intelligent Systems and Technology, Vol. 8, No. 4, Article 58, 15 pages, February 2017.
  8. Akshi Kumar and Nitin Sachdeva, “Cyberbullying detection on social multimedia using soft computing techniques: a meta-analysis”, Springer on Multimedia Tools and Applications, pages:23973–24010, 23 January 2019.
  9. Semiu Salawu, Yulan He, and Joanna Lumsden, “Approaches to Automated Detection of Cyberbullying: A Survey”, IEEE Transactions on Affective Computing, Vol. 11, Issue: 1, pages: 3-24, 10 October 2017.
  10. Mengfan Yao, Charalampos Chelmis and Daphney–Stavroula Zois, “Cyberbullying Detection on Instagram with Optimal Online Feature Selection”, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 28-31 august 2018.
  11. Mengfan Yao, Charalampos Chelmis, and Daphney–Stavroula Zois. 2019. “Cyberbullying Ends Here: Towards Robust Detection of Cyberbullying in Social Media”. In Proceedings of the 2019 World Wide Web Conference (WWW’19), Pages 3427-3433 13–17 May 2019.
  12. S.V.Drishya and J.I.Sheeba” Cyberbully Images and Text Detection using convolutional Neural networks” CiiT International Journal of Fuzzy Systems, Vol 11, No 2, April - June 2019
  13. Revathy Cadiravane and J.I.Sheeba “Identification and Classification of Cyberbully Incidents using Bystander Intervention Model” IJRTE, ISSN: 2277-3878, Volume-8 Issue-2S4, July 2019
  14. B.Sri Nandhini, J.I.Sheeba,: Cyberbullying Detection and Classification Using Information Retrieval Algorithm. In: ICARCSET '15, ACM, pp.1-5 (2015)
  15. Lu Cheng, Ruocheng Guo, Yasin Silva, Deborah Hall, and Huan Liu. 2019. Hierarchical Attention Networks for Cyberbullying Detection on the Instagram Social Network. In SDM, pages:235-243.
  16. [Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. “Neural machine translation by jointly learning to align and translate”. Published as a conference paper at ICLR 2015, pages: 1-15.
  17. Aditya Grover, Aaron Zweig, and Stefano Ermon. “Graphite: Iterative generative modeling of graphs”. Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2434-2444, 2019.
  18. Guillaume Salha, Romain Hennequin, Viet Anh Tran, and Michalis Vazirgiannis. “A degeneracy framework for scalable graph auto encoders”. arXiv preprint arXiv:1902.08813 (2019).
  19. Shuangfei Zhai, Yu Cheng, Weining Lu, and Zhongfei Zhang. “Deep structured energy related models for anomaly detection”. Proceedings of the 33rd International Conference on Machine Learning, PMLR 48:1100-1109, 2016.
  20. Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen. 2018. “Deep auto encoding gaussian mixture model for unsupervised anomaly detection”. In ICLR, pages:1-19.
  21. Lu Cheng, Kai Shu, Siqi Wu, Yasin N. Silva, Deborah L. Hall, Huan Liu. 2020. “Unsupervised Cyberbullying Detection via Time-Informed Gaussian Mixture Model”. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM ’20), October 19–23, 2020, Virtual Event, Ireland. ACM, New York, NY, USA, 10 pages.
  22. Dinakar, K., Jones, B., Havasi, C., Lieberman, H., & Picard, R. (2012). Common Sense Reasoning for Detection, Prevention, and Mitigation of Cyberbullying. ACM Transactions on Interactive Intelligent Systems, 2(3), 1–30.
  23. Devin Soni and Vivek Singh. 2018. “Time Reveals All Wounds: Modeling Temporal Characteristics of Cyberbullying”. Proceedings of the International AAAI Conference on Web and Social Media, 12(1), pages: 648-687.
Index Terms

Computer Science
Information Sciences


Cyberbullying Instagram Multimodal Hybrid Unsupervised