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Enhancing Cyberbullying Detection in Bangla Language: A Hybrid BiLSTM-Attention Approach

by Sultana Umme Habiba, Sadia Sharmin
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 21
Year of Publication: 2025
Authors: Sultana Umme Habiba, Sadia Sharmin
10.5120/ijca2025925105

Sultana Umme Habiba, Sadia Sharmin . Enhancing Cyberbullying Detection in Bangla Language: A Hybrid BiLSTM-Attention Approach. International Journal of Computer Applications. 187, 21 ( Jul 2025), 13-19. DOI=10.5120/ijca2025925105

@article{ 10.5120/ijca2025925105,
author = { Sultana Umme Habiba, Sadia Sharmin },
title = { Enhancing Cyberbullying Detection in Bangla Language: A Hybrid BiLSTM-Attention Approach },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2025 },
volume = { 187 },
number = { 21 },
month = { Jul },
year = { 2025 },
issn = { 0975-8887 },
pages = { 13-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number21/enhancing-cyberbullying-detection-in-bangla-language-a-hybrid-bilstm-attention-approach/ },
doi = { 10.5120/ijca2025925105 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-07-26T00:55:56.395665+05:30
%A Sultana Umme Habiba
%A Sadia Sharmin
%T Enhancing Cyberbullying Detection in Bangla Language: A Hybrid BiLSTM-Attention Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 21
%P 13-19
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the rapid advancement in the field of information and communication technology, people are getting connected with each other via social media sharing content like texts, images or posts. Since the trend of sharing thoughts, feelings or opinions through social media has become an indispensable part of our life, social media platforms have opened the way of being a victim of cyberbullying significantly more than before. Social distancing, due to the effect of the post COVID 19 pandemic situation, causes a noteworthy rise up to be a victim of cyberbullying in social media. This work proposes a hybrid deep learning based classifier that combines a self-attention layer with BiLSTM to differentiate between bully and non-bully texts in Bangla language from different social media. We have collected and labelled our work dataset from Facebook, YouTube, Twitter, TikTok etc. Context-based data augmentation is applied to improve the performance of the model. Existing algorithms for sentiment analysis tasks like SVM, Random Forest, Naive Bayes, LSTM, GRU, BERT etc. are experimented and comparative analysis among these models and our proposed hybrid model is also demonstrated. This research combines prominent feature extraction techniques like count vectorizer, Tf-Idf, and transformer-based contextual word embedding. The experimental result depicts that our proposed hybrid model outperforms all the previous works in cyberbullying detection in Bangla by achieving 89.3% accuracy.

References
  1. Han, Ziqiang, Wang, Ziyi, & Li, Yuhuan. (2021). Cyberbullying involvement, resilient coping, and loneliness of adolescents during Covid-19 in rural China. Frontiers in Psychology, 12, 2275. Frontiers.
  2. The Daily Star. (2016). 49% Bangladeshi school pupils face cyberbullying. Retrieved from https://www.thedailystar.net/ bytes/-bangladeshi-school-pupils-face-cyberbullying-287209 [Online; accessed 19-July-2022].
  3. Wahid, Md Ferdous, Hasan, Md Jahid, & Alom, Md Shahin. (2019). Cricket sentiment analysis from Bangla text using recurrent neural network with long short term memory model. In 2019 International Conference on Bangla Speech and Language Processing (ICBSLP) (pp. 1-4). IEEE.
  4. Khan, Md Serajus Salekin, Rafa, Sanjida Reza, & Das, Amit Kumar. (2021). Sentiment Analysis on Bengali Facebook Comments To Predict Fan's Emotions Towards a Celebrity. Journal of Engineering Advancements, 2(03), 118-124.
  5. Alvi, Nasif, & Talukder, Kamrul Hasan. (2021). Sentiment Analysis of Bengali Text using CountVectorizer with Logistic Regression. In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 01-05). IEEE.
  6. Bhowmik, Nitish Ranjan, Arifuzzaman, Mohammad, Mondal, M Rubaiyat Hossain, & Islam, MS. (2021). Bangla text sentiment analysis using supervised machine learning with extended lexicon dictionary. Natural Language Processing Research, 1(3-4), 34-45. Atlantis Press.
  7. Liebeskind, Chaya, & Liebeskind, Shmuel. (2018). Identifying abusive comments in Hebrew Facebook. In 2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE) (pp. 1-5). IEEE.
  8. Jahan, Maliha, Ahamed, Istiak, Bishwas, Md Rayanuzzaman, & Shatabda, Swakkhar. (2019). Abusive comments detection in Bangla-English code-mixed and transliterated text. In 2019 2nd International Conference on Innovation in Engineering and Technology (ICIET) (pp. 1-6). IEEE.
  9. Sharmin, Sadia, Chakma, Danial. (2021). Attention-based convolutional neural network for Bangla sentiment analysis. Ai & Society, 36(1), 381-396. Springer.
  10. Van Hee, Cynthia, Jacobs, Gilles, Emmery, Chris, Desmet, Bart, Lefever, Els, Verhoeven, Ben, De Pauw, Guy, Daelemans, Walter, & Hoste, V ronique. (2018). Automatic detection of cyberbullying in social media text. PloS one, 13(10), e0203794. Public Library of Science San Francisco, CA USA.
  11. Perera, Andrea, & Fernando, Pumudu. (2021). Accurate cyberbullying detection and prevention on social media. Procedia Computer Science, 181, 605-611. Elsevier.
  12. Murshed, Belal Abdullah Hezam, Abawajy, Jemal, Mallappa, Suresha, Saif, Mufeed Ahmed Naji, & Al-Ariki, Hasib Daowd Esmail. (2022). DEA-RNN: A Hybrid Deep Learning Approach for Cyberbullying Detection in Twitter Social Media Platform. IEEE Access, 10, 25857-25871. IEEE.
  13. Tan, Kian Long, Lee, Chin Poo, Anbananthen, Kalaiarasi Sonai Muthu, & Lim, Kian Ming. (2022). RoBERTa-LSTM: A Hybrid Model for Sentiment Analysis With Transformer and Recurrent Neural Network. IEEE Access, 10, 21517-21525. IEEE.
  14. Islam, Md Manowarul, Uddin, Md Ashraf, Islam, Linta, Akter, Arnisha, Sharmin, Selina, & Acharjee, Uzzal Kumar. (2020). Cy-berbullying detection on social networks using machine learning approaches. In 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) (pp. 1-6). IEEE.
  15. Ahmed, Md Faisal, Mahmud, Zalish, Biash, Zarin Tasnim, Ryen, Ahmed Ann Noor, Hossain, Arman, & Ashraf, Faisal Bin. (2021). Cy-berbullying detection using deep neural networks from social media comments in Bangla language. arXiv preprint arXiv:2106.04506.
  16. Desai, Aditya, Kalaskar, Shashank, Kumbhar, Omkar, & Dhumal, Rashmi. (2021). Cyber Bullying Detection on Social Media using Machine Learning. In ITM Web of Conferences (Vol. 40, p. 03038). EDP Sciences.
  17. Vijayakumar, V, Prasad, Hari, & Adlof, P. (2021). Multimodal Cyberbullying Detection using Hybrid Deep Learning Algorithms. In International Journal of Applied Engineering Research (Vol. 16, pp. 568-574).
  18. Dewani, Amirita, Memon, Mohsin Ali, & Bhatti, Sania. (2021). Cyberbullying detection: advanced preprocessing techniques & deep learning architecture for Roman Urdu data. Journal of big data, 8(1), 1-20. Springer.
  19. Al-Ajlan, Monirah Abdullah, & Ykhlef, Mourad. (2018). Deep learning algorithm for cyberbullying detection. International Journal of Advanced Computer Science and Applications, 9(9). Science and Information (SAI) Organization Limited.
  20. Roy, Pradeep Kumar, & Mali, Fenish Umeshbhai. (2022). Cyberbullying detection using deep transfer learning. Complex & Intelligent Systems, 1-19. Springer.
  21. Bharti, Shubham, Yadav, Arun Kumar, Kumar, Mohit, & Yadav, Divakar. (2021). Cyberbullying detection from tweets using deep learning. Kybernetes. Emerald Publishing Limited.
  22. Hoq, Muntasir, Haque, Promila, & Uddin, Mohammed Nazim. (2021). Sentiment analysis of Bangla language using deep learning approaches. In International Conference on Computing Science, Communication and Security (pp. 140-151). Springer.
  23. Hossain Junaid, Mohd. Istiaq, Hossain, Faisal, Upal, Udyan Saha, Tameem, Anjana, & Kashim, Abul. (2022). Bangla Food Review Sentimental Analysis using Machine Learning. In 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0347-0353). IEEE.
  24. Rahman, Moqsadur, Haque, Summit, & Saurav, Zillur Rahman. (2020). Identifying and categorizing opinions expressed in Bangla sentences using deep learning technique. International Journal of Computer Applications, 975, 8887.
  25. Veeranki Lakshmi Durga, & A. Mary Sowjanya. (2020). SENTIMENT ANALYSIS ON BANGLA YOUTUBE COMMENTS USING MACHINE LEARNING TECHNIQUES. Journal of emerging technologies and innovative research.
  26. Khan, Md Serajus Salekin, Rafa, Sanjida Reza, Das, Amit Kumar, & others. (2021). Sentiment Analysis on Bengali Facebook Comments To Predict Fan's Emotions Towards a Celebrity. Journal of Engineering Advancements, 2(03), 118-124.
  27. Quinlan, J. Ross. (1986). Induction of decision trees. Machine learning, 1(1), 81-106. Springer.
  28. Breiman, Leo. (2001). Random forests. Machine learning, 45(1), 5-32. Springer.
  29. Ukil, Abhisek. (2007). Support vector machine. In Intelligent Systems and Signal Processing in Power Engineering (pp. 161-226). Springer.
  30. Leung, K Ming. (2007). Naive bayesian classifier. Polytechnic University Department of Computer Science/Finance and Risk Engineering, 2007, 123-156.
  31. Van Houdt, Greg, Mosquera, Carlos, & N poles, Gonzalo. (2020). A review on the long short-term memory model. Artificial Intelligence Review, 53(8), 5929-5955. Springer.
  32. Zhang, Shu, Zheng, Dequan, Hu, Xinchen, & Yang, Ming. (2015). Bidirectional long short-term memory networks for relation classification. In Proceedings of the 29th Pacific Asia conference on language, information and computation (pp. 73-78).
  33. Shen, Guizhu, Tan, Qingping, Zhang, Haoyu, Zeng, Ping, & Xu, Jianjun. (2018). Deep learning with gated recurrent unit networks for financial sequence predictions. Procedia computer science, 131, 895-903. Elsevier.
  34. Devlin, Jacob, Chang, Ming-Wei, Lee, Kenton, & Toutanova, Kristina. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
  35. Brauwers, Gianni, & Frasincar, Flavius. (2021). A general survey on attention mechanisms in deep learning. IEEE Transactions on Knowledge and Data Engineering. IEEE.
  36. Bhattacharjee, Abhik, Hasan, Tahmid, Samin, Kazi, Rahman, M. Sohel, Iqbal, Anindya, & Shahriyar, Rifat. (2021). BanglaBERT: Combating Embedding Barrier for Low-Resource Language Understanding. CoRR, abs/2101.00204.
  37. Karim, Md. Rezaul, Chakravarthi, Bharathi Raja, McCrae, John P., & Cochez, Michael. (2020). Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network. CoRR, abs/2004.07807.
  38. Liu, Yinhan, Ott, Myle, Goyal, Naman, Du, Jingfei, Joshi, Mandar, Chen, Danqi, Levy, Omer, Lewis, Mike, Zettlemoyer, Luke, & Stoyanov, Veselin. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. CoRR, abs/1907.11692.
  39. Sanh, Victor, Debut, Lysandre, Chaumond, Julien, & Wolf, Thomas. (2019). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108.
Index Terms

Computer Science
Information Sciences

Keywords

Cyberbullying detection Deep learning Attention

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