Skip to main content
SpringerLink
Log in

Bangla Social Media Cyberbullying Detection Using Deep Learning

  • Conference paper
  • First Online:
Intelligent Systems and Data Science (ISDS 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1949))

Included in the following conference series:

  • 361 Accesses

Abstract

The growth of social media over the past decade has been nothing short of phenomenal. An exponential increase has been seen on platforms like Facebook, Twitter, Instagram, LinkedIn, and YouTube, in their user base, accumulating billions of active users worldwide. Technology advancements, the ubiquitous use of smartphones, and the innate human desire for connection don’t always contribute constructively; they might additionally end up in spreading violence in the form of bullying of others which is known as cyberbullying. As a result of cyberbullying, the victims will frequently experience anxiety, depression, and other mental diseases which can even result in suicide. Therefore the extensive need of detecting and controlling cyberbullying motivated us to automate this process. In this paper, we have introduced an approach to detect cyberbullying from social media data by using deep learning models. We have used Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network in the proposed hybrid approach along with word embedding technique called fastText. We achieved an accuracy of 91.63% with the proposed model by using a publicly available dataset containing 16,073 samples which outperformed all the state of the art models.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Social Media in Bangladesh - 2023 Stats Platform Trends - OOSGA – oosga.com

    Google Scholar 

  2. Ahammed, S., Rahman, M., Niloy, M.H., Chowdhury, S.M.H.: Implementation of machine learning to detect hate speech in Bangla language. In: 2019 8th International Conference System Modeling and Advancement in Research Trends (SMART), pp. 317–320. IEEE (2019)

    Google Scholar 

  3. Ahmed, M.F., Mahmud, Z., Biash, Z.T., Ryen, A.A.N., Hossain, A., Ashraf, F.B.: Cyberbullying detection using deep neural network from social media comments in Bangla language. arXiv preprint arXiv:2106.04506 (2021)

  4. Arreerard, R., Senivongse, T.: Thai defamatory text classification on social media. In: 2018 IEEE International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD), pp. 73–78. IEEE (2018)

    Google Scholar 

  5. Aurpa, T.T., Sadik, R., Ahmed, M.S.: Abusive Bangla comments detection on Facebook using transformer-based deep learning models. Soc. Netw. Anal. Min. 12(1), 24 (2022)

    Article  Google Scholar 

  6. Banik, N., Rahman, M.H.H.: Toxicity detection on Bengali social media comments using supervised models. In: 2019 2nd International Conference on Innovation in Engineering and Technology (ICIET), pp. 1–5. IEEE (2019)

    Google Scholar 

  7. Belal, T.A., Shahariar, G., Kabir, M.H.: Interpretable multi labeled Bengali toxic comments classification using deep learning. In: 2023 International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 1–6. IEEE (2023)

    Google Scholar 

  8. Cecillon, N., Labatut, V., Dufour, R., Linarès, G.: Abusive language detection in online conversations by combining content-and graph-based features. Front. Big Data 2, 8 (2019)

    Article  Google Scholar 

  9. Chakraborty, P., Seddiqui, M.H.: Threat and abusive language detection on social media in Bengali language. In: 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), pp. 1–6. IEEE (2019)

    Google Scholar 

  10. Das, A.K., Asif, A.A., Paul, A., Hossain, M.N.: Bangla hate speech detection on social media using attention-based recurrent neural network. J. Intell. Syst. 30(1), 578–591 (2021). https://doi.org/10.1515/jisys-2020-0060

    Article  Google Scholar 

  11. Gers, F.: Learning to forget: continual prediction with LSTM. In: 9th International Conference on Artificial Neural Networks: ICANN 1999 (1999). https://doi.org/10.1049/cp:19991218

  12. Ghosh, T., Chowdhury, A.A.K., Banna, M.H.A., Nahian, M.J.A., Kaiser, M.S., Mahmud, M.: A hybrid deep learning approach to detect Bangla social media hate speech. In: Hossain, S., Hossain, M.S., Kaiser, M.S., Majumder, S.P., Ray, K. (eds.) Proceedings of International Conference on Fourth Industrial Revolution and Beyond 2021, pp. 711–722. Springer, Cham (2022). https://doi.org/10.1007/978-981-19-2445-3_50

    Chapter  Google Scholar 

  13. Gordeev, D.: Automatic detection of verbal aggression for Russian and American imageboards. Procedia. Soc. Behav. Sci. 236, 71–75 (2016)

    Article  Google Scholar 

  14. Haidar, B., Chamoun, M., Serhrouchni, A.: Multilingual cyberbullying detection system: detecting cyberbullying in Arabic content. In: 2017 1st Cyber Security in Networking Conference (CSNet), pp. 1–8. IEEE (2017)

    Google Scholar 

  15. Haidar, B., Chamoun, M., Serhrouchni, A.: A multilingual system for cyberbullying detection: Arabic content detection using machine learning. Adv. Sci. Technol. Eng. Syst. J. 2(6), 275–284 (2017)

    Article  Google Scholar 

  16. Social Media User Statistics: How Many People Use Social Media? searchlogistics.com. https://www.facebook.com/mattwoodwarduk. https://www.searchlogistics.com/learn/statistics/social-media-user-statistics/. Accessed 12 July 2023

  17. Huan, J.L., Sekh, A.A., Quek, C., Prasad, D.K.: Emotionally charged text classification with deep learning and sentiment semantic. Neural Comput. Appl. 34(3), 2341–2351 (2021). https://doi.org/10.1007/s00521-021-06542-1

    Article  Google Scholar 

  18. Ibrohim, M.O., Budi, I.: Multi-label hate speech and abusive language detection in Indonesian twitter. In: Proceedings of the Third Workshop on Abusive Language Online, pp. 46–57 (2019)

    Google Scholar 

  19. Karim, M.R., Chakravarthi, B.R., McCrae, J.P., Cochez, M.: Classification benchmarks for under-resourced Bengali language based on multichannel convolutional-LSTM network. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pp. 390–399. IEEE (2020)

    Google Scholar 

  20. Kumar, A., Sachdeva, N.: Multi-input integrative learning using deep neural networks and transfer learning for cyberbullying detection in real-time code-mix data. Multimedia Syst. 28(6), 2027–2041 (2022)

    Article  Google Scholar 

  21. Lane, J.: The 10 most spoken languages in the world (2023). https://www.babbel.com/en/magazine/the-10-most-spoken-languages-in-the-world

  22. Luan, Y., Lin, S.: Research on text classification based on CNN and LSTM. In: 2019 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), pp. 352–355. IEEE (2019)

    Google Scholar 

  23. Luo, X.: Efficient English text classification using selected machine learning techniques. Alex. Eng. J. 60(3), 3401–3409 (2021)

    Article  Google Scholar 

  24. Malik, P., Aggrawal, A., Vishwakarma, D.K.: Toxic speech detection using traditional machine learning models and BERT and fasttext embedding with deep neural networks. In: 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), pp. 1254–1259. IEEE (2021)

    Google Scholar 

  25. Mohammed, A., Kora, R.: An effective ensemble deep learning framework for text classification. J. King Saud Univ.-Comput. Inf. Sci. 34(10), 8825–8837 (2022)

    Google Scholar 

  26. Pawar, R., Raje, R.R.: Multilingual cyberbullying detection system. In: 2019 IEEE International Conference on Electro Information Technology (EIT), pp. 040–044. IEEE (2019)

    Google Scholar 

  27. Pratiwi, N.I., Budi, I., Jiwanggi, M.A.: Hate speech identification using the hate codes for Indonesian tweets. In: Proceedings of the 2019 2nd International Conference on Data Science and Information Technology, pp. 128–133 (2019)

    Google Scholar 

  28. Ptaszynski, M., Pieciukiewicz, A., Dybała, P.: Results of the poleval 2019 shared task 6: first dataset and open shared task for automatic cyberbullying detection in polish twitter (2019)

    Google Scholar 

  29. Ritu, S.S., Mondal, J., Mia, M.M., Al Marouf, A.: Bangla abusive language detection using machine learning on radio message gateway. In: 2021 6th International Conference on Communication and Electronics Systems (ICCES), pp. 1725–1729. IEEE (2021)

    Google Scholar 

  30. Sazzed, S.: Abusive content detection in transliterated Bengali-English social media corpus. In: Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching, pp. 125–130 (2021)

    Google Scholar 

  31. Team, B.: All the latest cyberbullying statistics for 2023 (2023). https://www.broadbandsearch.net/blog/cyber-bullying-statistics

  32. Yuvaraj, N., et al.: Automatic detection of cyberbullying using multi-feature based artificial intelligence with deep decision tree classification. Comput. Electr. Eng. 92, 107186 (2021)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information and Communication Technology, Bangladesh University of Professionals, Mirpur Cantonment, Dhaka, Bangladesh

    Anika Tasnim Rodela

  2. College of Information and Communication Technology, Can Tho University, 3/2 Street, Ninh Kieu District, Can Tho City, Vietnam

    Huu-Hoa Nguyen

  3. Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Badda, Dhaka, 1212, Bangladesh

    Dewan Md. Farid & Mohammad Nurul Huda

Authors
  1. Anika Tasnim Rodela
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huu-Hoa Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dewan Md. Farid
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohammad Nurul Huda
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dewan Md. Farid .

Editor information

Editors and Affiliations

  1. Can Tho University, Can Tho, Vietnam

    Nguyen Thai-Nghe

  2. Can Tho University, Can Tho, Vietnam

    Thanh-Nghi Do

  3. Mahidol University, Salaya, Thailand

    Peter Haddawy

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rodela, A.T., Nguyen, HH., Farid, D.M., Huda, M.N. (2024). Bangla Social Media Cyberbullying Detection Using Deep Learning. In: Thai-Nghe, N., Do, TN., Haddawy, P. (eds) Intelligent Systems and Data Science. ISDS 2023. Communications in Computer and Information Science, vol 1949. Springer, Singapore. https://doi.org/10.1007/978-981-99-7649-2_13

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-7649-2_13

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-7648-5

  • Online ISBN: 978-981-99-7649-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation