Cyberbullying detection: advanced preprocessing techniques & deep learning architecture for Roman Urdu data
Open accessAbstract
Social media have become a very viable medium for communication, collaboration, exchange of information, knowledge, and ideas. However, due to anonymity preservation, the incidents of hate speech and cyberbullying have been diversified across the globe. This intimidating problem has recently sought the attention of researchers and scholars worldwide and studies have been undertaken to formulate solution strategies for automatic detection of cyberaggression and hate speech, varying from machine learning models with vast features to more complex deep neural network models and different SN platforms. However, the existing research is directed towards mature languages and highlights a huge gap in newly embraced resource poor languages. One such language that has been recently adopted worldwide and more specifically by south Asian countries for communication on social media is Roman Urdu i-e Urdu language written using Roman scripting. To address this research gap, we have performed extensive preprocessing on Roman Urdu microtext. This typically involves formation of Roman Urdu slang- phrase dictionary and mapping slangs after tokenization. We have also eliminated cyberbullying domain specific stop words for dimensionality reduction of corpus. The unstructured data were further processed to handle encoded text formats and metadata/non-linguistic features. Furthermore, we performed extensive experiments by implementing RNN-LSTM, RNN-BiLSTM and CNN models varying epochs executions, model layers and tuning hyperparameters to analyze and uncover cyberbullying textual patterns in Roman Urdu. The efficiency and performance of models were evaluated using different metrics to present the comparative analysis. Results highlight that RNN-LSTM and RNN-BiLSTM performed best and achieved validation accuracy of 85.5 and 85% whereas F1 score was 0.7 and 0.67 respectively over aggression class.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Keras is a popular framework for cyberbullying detection.
“However, Keras is also a popular framework [20,25,171,184] for cyberbullying detection since the framework is built to provide a simple interface for quick prototyping by building active neural networks that can work upon TensorFlow.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [184]
The Experimental setup section says the authors implemented and trained their cyberbullying-detection models in Keras, supporting its use for that purpose. Our copy does not establish that Keras is popular for cyberbullying detection. (LLM, conf=0.82)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM
Shahid, F., Zameer, A., & Muneeb, M. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.110212 · Cited by 744 · see citing works
The impact of preprocessing on text classification
Uysal, A. K., & Günal, S. (2013) · Information Processing & Management · doi:10.1016/j.ipm.2013.08.006 · Cited by 664 · see citing works
Sentiment Analysis of Comment Texts Based on BiLSTM
Xu, G., Meng, Y., Qiu, X., Yu, Z., & Wu, X. (2019) · IEEE Access · doi:10.1109/access.2019.2909919 · Cited by 650 · see citing works
Modeling the Detection of Textual Cyberbullying
Dinakar, K., Reichart, R., & Lieberman, H. A. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i3.14209 · Cited by 517 · see citing works
Automatic detection of cyberbullying in social media text
Hee, C. V., Jacobs, G., Emmery, C., Desmet, B., Lefever, E., Verhoeven, B., Pauw, G. D., Daelemans, W. M. P., & Hoste, V. (2018) · PLoS ONE · doi:10.1371/journal.pone.0203794 · Cited by 403 · see citing works
Developing an online hate classifier for multiple social media platforms
Salminen, J. O., Hopf, M., Chowdhury, S. A., Jung, S., Almerekhi, H., & Jansen, B. J. J. (2020) · Human-centric Computing and Information Sciences · doi:10.1186/s13673-019-0205-6 · Cited by 295 · see citing works
Hellfeldt, K., López-Romero, L., & Andershed, H. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17010045 · Cited by 278 · see citing works
A Dataset of Hindi-English Code-Mixed Social Media Text for Hate Speech Detection
Bohra, A., Vijay, D., Singh, V. K., Akhtar, S. S., & Shrivastava, M. (2018) · doi:10.18653/v1/w18-1105 · Cited by 272 · see citing works
BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs
Cliche, M. (2017) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s17-2094 · Cited by 248 · see citing works
Cyberbullying detection solutions based on deep learning architectures
Iwendi, C., Srivastava, G., Khan, S., & Maddikunta, P. K. R. (2023) · Multimedia Systems · doi:10.1007/s00530-020-00701-5 · Cited by 205 · see citing works
A Multilingual System for Cyberbullying Detection: Arabic Content Detection using Machine Learning
Haidar, B. R., Chamoun, M., & Serhrouchni, A. (2017) · Advances in Science Technology and Engineering Systems Journal · doi:10.25046/aj020634 · Cited by 117 · see citing works
A Multichannel Deep Learning Framework for Cyberbullying Detection on Social Media
Alotaibi, M., Alotaibi, B., & Razaque, A. (2021) · Electronics · doi:10.3390/electronics10212664 · Cited by 94 · see citing works
A Multichannel Deep Learning Framework for Cyberbullying Detection on Social Media
Alotaibi, M., Alotaibi, B., & Razaque, A. (2021) · doi:10.20944/preprints202110.0070.v1 · Cited by 94 · cited 1× · see citing works
“Due to the accretion of social media communication and adverse effects arising from its darker side on users, the field of automatic cyberbullying detection has become an emerging and evolving research trend [8].”
BackgroundDetection of cyberbullying on social media messages in Turkish
Özel, S. A., Eşsiz, E. S., Akdemir, S., & Aksu, H. (2017) · doi:10.1109/ubmk.2017.8093411 · Cited by 87 · see citing works
Social media bullying detection using machine learning on Bangla text
Abdhullah-Al-Mamun, & Akhter, S. (2018) · International Conference on Electrical and Computer Engineering (ICECE) · doi:10.1109/icece.2018.8636797 · Cited by 78 · see citing works
A Precisely Xtreme-Multi Channel Hybrid Approach for Roman Urdu Sentiment Analysis
Mehmood, F., Khan, M. U. G., Ibrahim, M. A., Shahzadi, R., Mahmood, W., & Asim, M. N. (2020) · IEEE Access · doi:10.1109/access.2020.3030885 · Cited by 53 · see citing works
Dessı̀, D., Recupero, D. R., & Sack, H. (2021) · Electronics · doi:10.3390/electronics10070779 · Cited by 50 · see citing works
Indonesian Twitter Cyberbullying Detection using Text Classification and User Credibility
Nurrahmi, H., & Nurjanah, D. (2018) · International Conference on Information and Communications Technology (ICOIACT) · doi:10.1109/icoiact.2018.8350758 · Cited by 47 · see citing works
Arif, M. (2021) · Journal of Information Security and Cybercrimes Research · doi:10.26735/gbtv9013 · Cited by 45 · see citing works
Dewani, A., Memon, M. A., & Bhatti, S. (2021) · 3C TIC Cuadernos de desarrollo aplicados a las TIC · doi:10.17993/3ctic.2021.102.101-121 · Cited by 27 · see citing works
Self-citationRUTUT: Roman Urdu to Urdu Translator Based on Character Substitution Rules and Unicode Mapping
Shahroz, M., Mushtaq, M. F., Mehmood, A., Ullah, S., & Choi, G. S. (2020) · IEEE Access · doi:10.1109/access.2020.3031393 · Cited by 13 · see citing works
Flagged first, then by citations
2 of the 23 references couldn’t be matched to a known paper.
Research works printed but not linked: 12
- [2]Dadvar M. Experts and machines united against cyberbullying [PhD thesis]. University of Twente. 2014.
- [14]E. C. Ates, E. Bostanci, and M. S. Guzel, Comparative Performance of Machine Learning Algorithms in Cyberbullying Detection: Using Turkish Language Preprocessing Techniques, arXiv Prepr. arXiv2101.12718, 2021.
- [17]Gómez-Adorno H, Bel-Enguix G, Sierra G, Sánchez O, Quezada D. A machine learning approach for detecting aggressive tweets in Spanish, In: IberEval@ SEPLN. 2018. pp. 102–107.
- [18]X. Bai, F. Merenda, C. Zaghi, T. Caselli, and M. Nissim, RuG at GermEval: Detecting Offensive Speech in German Social Media, in 14th Conference on Natural Language Processing KONVENS 2018, 2018, p. 63.
- [19]B. Birkeneder, J. Mitrovic, J. Niemeier, L. Teubert, and S. Handschuh, upInf—Offensive Language Detection in German Tweets, In: Proceedings of the GermEval 2018 Workshop, 2018, pp. 71–78.
- [20]J. M. Schneider, R. Roller, P. Bourgonje, S. Hegele, and G. Rehm, Towards the Automatic Classification of Offensive Language and Related Phenomena in German Tweets, In: 14th Conference on Natural Language Processing KONVENS 2018, 2018, p. 95.
- [21]H. Margono, X. Yi, and G. K. Raikundalia, Mining Indonesian cyber bullying patterns in social networks, In: Proceedings of the Thirty-Seventh Australasian Computer Science Conference-Volume 147, 2014, pp. 115–124.
- [24]A. Roy, P. Kapil, K. Basak, and A. Ekbal, An ensemble approach for aggression identification in english and hindi text, In: Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018), 2018, pp. 66–73.
- [25]Association for Computational Linguistics. https://www.aclweb.org/portal/content/deadline-extension-first-task-automatic-cyberbullying-detection-polish-language. Accessed 09 May 2019.
- [27]Talpur KR Yuhaniz SS Sjarif NNBA Ali B Cyberbullying detection in Roman Urdu language using lexicon based approach J Crit Rev 2020 7 16 834 848 10.31838/jcr.07.16.109 · DOI
- [33]W. Zaremba, I. Sutskever, and O. Vinyals, Recurrent neural network regularization, arXiv Prepr. arXiv1409.2329, 2014.
- [35]S. Minaee, E. Azimi, and A. Abdolrashidi, Deep-sentiment: Sentiment analysis using ensemble of cnn and bi-lstm models, arXiv Prepr. arXiv1904.04206, 2019.
Cited by
Sorted by citations
Cyberbullying detection for low-resource languages and dialects: Review of the state of the art
Mahmud, T., Ptaszynski, M. E., Eronen, J., & Masui, F. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103454 · Cited by 172 · see citing works
A Review on Deep-Learning-Based Cyberbullying Detection
Hasan, M. T., Hossain, M. A. E., Mukta, M. S. H., Akter, A., Ahmed, M., & Islam, S. (2023) · Future Internet · doi:10.3390/fi15050179 · Cited by 103 · see citing works
“However, Keras is also a popular framework [20,25,171,184] for cyberbullying detection since the framework is built to provide a simple interface for quick prototyping by building active neural networks that can work upon TensorFlow.”
BackgroundAn application to detect cyberbullying using machine learning and deep learning techniques
Raj, M., Singh, S., Solanki, K., & Selvanambi, R. (2022) · SN Computer Science · doi:10.1007/s42979-022-01308-5 · Cited by 97 · see citing works
Hate Speech Detection in Social Networks using Machine Learning and Deep Learning Methods
Toktarova, A., Syrlybay, D., Myrzakhmetova, B., Anuarbekova, G., Rakhimbayeva, G., Zhylanbaeva, B., Suieuova, N., & Kerimbekov, M. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140542 · Cited by 53 · see citing works
A Deep Analysis of Textual Features Based Cyberbullying Detection Using Machine Learning
Mahmud, M. I., Mamun, M., & Abdelgawad, A. (2022) · IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) · doi:10.1109/gcaiot57150.2022.10019058 · Cited by 41 · see citing works
Cyberbullying Detection and Severity Determination Model
Obaida, M. H., Guirguis, S. K., & Elkaffas, S. M. (2023) · IEEE Access · doi:10.1109/access.2023.3313113 · Cited by 44 · see citing works
Dewani, A., Memon, M. A., Bhatti, S., Sulaiman, A. A., Hamdi, M., Alshahrani, H. M., Alghamdi, A., & Shaikh, A. (2023) · Applied Sciences · doi:10.3390/app13042062 · Cited by 34 · see citing works
Self-citationCyberbullying-related Hate Speech Detection Using Shallow-to-deep Learning
Sultan, D., Toktarova, A., Zhumadillayeva, А., Aldeshov, S., Mussiraliyeva, S., Beissenova, G., Tursynbayev, A., Baenova, G., & Imanbayeva, A. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.032993 · Cited by 33 · see citing works
Privacy, Safety, and Security in Extended Reality: User Experience Challenges for Neurodiverse Users
Jones, D., Ghasemi, S., Gračanin, D., & Azab, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35822-7_33 · Cited by 30 · see citing works
Chinese Cyberbullying Detection Using XLNet and Deep Bi-LSTM Hybrid Model
Chen, S., Wang, J., & He, K. (2024) · Information · doi:10.3390/info15020093 · Cited by 29 · see citing works
Exploring Deep Learning for Chittagonian Slang Detection in Social Media Texts
Naher, S. R., Sultana, S., Mahmud, T., Aziz, M. T., Hossain, M. S., & Andersson, K. (2024) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet61485.2024.10698491 · Cited by 29 · see citing works
Sihab-Us-Sakib, S., Rahman, M. R., Forhad, M. S. A., & Aziz, M. A. (2024) · Natural Language Processing Journal · doi:10.1016/j.nlp.2024.100104 · Cited by 28 · see citing works
Abusive Language Detection in Urdu Text: Leveraging Deep Learning and Attention Mechanism
Khan, A., Ahmed, A., Jan, S., Bilal, M., & Zuhairi, M. F. (2024) · IEEE Access · doi:10.1109/access.2024.3370232 · Cited by 27 · see citing works
Cyberbullying Text Identification based on Deep Learning and Transformer-based Language Models
Saifullah, K., Khan, M. I., Jamal, S., & Sarker, I. H. (2024) · EAI Endorsed Transactions on Industrial Networks and Intelligent Systems · doi:10.4108/eetinis.v11i1.4703 · Cited by 24 · see citing works
Hate Speech Detection Research in South Asian Languages: A Survey of Tasks, Datasets and Methods
Sharma, D., Nath, T., Gupta, V., & Singh, V. K. (2025) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3711710 · Cited by 23 · see citing works
Razi, F. D., & Ejaz, N. (2024) · IEEE Access · doi:10.1109/access.2024.3432908 · Cited by 21 · see citing works
Tabassum, I., & Nunavath, V. (2024) · Applied Sciences · doi:10.3390/app142412007 · Cited by 19 · see citing works
Deep Learning Algorithms for Cyber-Bulling Detection in Social Media Platforms
Obaida, M. H., Elkaffas, S. M., & Guirguis, S. K. (2024) · IEEE Access · doi:10.1109/access.2024.3406595 · Cited by 22 · see citing works
Nasution, M. A. S., & Setiawan, E. B. (2023) · Revue d intelligence artificielle · doi:10.18280/ria.370413 · Cited by 12 · see citing works
Artificial Intelligence in Detecting and Preventing Online Harassment
Vyawahare, H. R., Khandelwal, S., & Rathod, S. B. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-1435-7.ch002 · Cited by 12 · see citing works
A Study of Cyberbullying Detection and Classification Techniques: A Machine Learning Approach
Unnava, S., & Rao, P. S. (2024) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.7621 · Cited by 11 · see citing works
Arabic Cyberbullying Detection: A Comprehensive Review of Datasets and Methodologies
Aljalaoud, H., Dashtipour, K. K., & Al‐Dubai, A. (2025) · IEEE Access · doi:10.1109/access.2025.3561132 · Cited by 10 · see citing works
Tiyasha, T., Onu, C. E., Ismail, M. A., Karri, R. R., Amari, A., Kumar, V., & Bhagat, S. K. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01110-z · Cited by 9 · see citing works
Investigating the cyberbullying risk in digital media: protecting victims in school teenagers
Obaidat, I., Al-zou’bi, A., Mughaid, A. S., & Abualigah, L. (2023) · Social Network Analysis and Mining · doi:10.1007/s13278-023-01152-2 · Cited by 9 · see citing works
Cyberbullying detection in Hinglish comments from social media using machine learning techniques
Kumar, S., Mondal, M., Dutta, T., & Singh, T. D. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19031-z · Cited by 7 · see citing works
Addressing cyberbullying in Urdu tweets: a comprehensive dataset and detection system
Adeeba, F., Yousuf, M. I., Anwer, I., Tariq, S. U., Ashfaq, A., & Naqeeb, M. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.1963 · Cited by 7 · see citing works
Dutta, S., Neog, M., & Baruah, N. (2024) · International Conference on Emerging Trends in Information Technology and Engineering (ICETITE) · doi:10.1109/ic-etite58242.2024.10493302 · Cited by 7 · see citing works
Amer, A. Y. A., Siddiqui, T., & Athamena, B. (2022) · Research Square · doi:10.21203/rs.3.rs-2184218/v1 · Cited by 7 · see citing works
Arabic cyberbullying detecting using ensemble deep learning technique
Ahmed, M. S., Maher, S. M., & Khudhur, M. E. (2023) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v32.i2.pp1031-1041 · Cited by 6 · see citing works
Gashroo, O. B., Mehrotra, M., Wani, M. A., & ELAffendi, M. A. (2025) · IEEE Access · doi:10.1109/access.2025.3533257 · Cited by 5 · see citing works
Explainable Artificial Intelligence for Combating Cyberbullying
Tesfagergish, S. G., & Damaševičius, R. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-53731-8_5 · Cited by 5 · see citing works
Wasid, M., Amar, A. Y. A., Ahmad, S., & Mustafa, J. (2025) · Life Cycle Reliability and Safety Engineering · doi:10.1007/s41872-025-00335-4 · Cited by 4 · see citing works
Unraveling Cyberbullying Dynamis: A Computational Framework Empowered by Artificial Intelligence
Barbosa-Santillán, L. I., Guzman-Velázquez, B. P., Orozco-Aguilera, M. T., & Flores-Pulido, L. (2025) · Information · doi:10.3390/info16020080 · Cited by 6 · see citing works
A Robust and Linguistically-Aware Hate Speech Detection System for Roman Urdu
Hashmi, E., Ahmad, H., Mazhar, M. T., Yayilgan, S. Y., Afzal, M., & Shaikh, S. (2025) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3768571 · Cited by 4 · see citing works
Arabic Cyberbullying Detection Using Machine Learning: State of the Art Survey
Alsunaidi, N., Aljbali, S., Yasin, Y., & Aljamaan, H. (2023) · International Conference on Evaluation and Assessment in Software Engineering (EASE) · doi:10.1145/3593434.3593968 · Cited by 4 · see citing works
Reddy, K. B., & Ramkumar, G. (2024) · AIP conference proceedings · doi:10.1063/5.0227903 · Cited by 4 · see citing works
A Comprehensive Survey on Urdu Hate Speech Detection: Methods, Evaluation, and Challenges
Hussain, I., Farooq, U., Cheema, A. N., & Almanjahie, I. M. (2025) · IEEE Access · doi:10.1109/access.2025.3591143 · Cited by 4 · see citing works
Enhancing Cyberbullying Detection in Bangla Language: A Hybrid BiLSTM-Attention Approach
Habiba, S. U., & Sharmin, S. (2025) · International Journal of Computer Applications · doi:10.5120/ijca2025925105 · Cited by 4 · see citing works
Hyperparameter Tuned Deep Learning Enabled Cyberbullying Classification in Social Media
Duhayyim, M. A., Mohamed, H. G., Alotaibi, S. S., Mahgoub, H., Mohamed, A., Motwakel, A., Zamani, A. S., & Eldesouki, M. I. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.031096 · Cited by 4 · see citing works
Cyberbullying Detection Approaches: A Review
Aljohani, E. J., Yafooz, W. M. S., & Alsaeedi, A. (2023) · International Conference on Inventive Research in Computing Applications (ICIRCA) · doi:10.1109/icirca57980.2023.10220688 · Cited by 6 · see citing works
Decoding Cyberbullying on Social Media: A Machine Learning Exploration
Saeid, A., Kanojia, D., & Neri, F. (2024) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai59869.2024.00084 · Cited by 5 · see citing works
Improving Cyberbullying Detection Through Adaptive External Dictionary in Machine LearningPreprint
Qudah, H., Abu-Alhaija, M., & Tarawneh, H. (2023) · Research Square · doi:10.21203/rs.3.rs-3306599/v1 · Cited by 4 · see citing works
Yang, Y., Wijewardane, N. K., Slonecki, T. J., Wadl, P. A., Andreason, S. A., Chen, J., & Harvey, L. M. (2025) · Smart Agricultural Technology · doi:10.1016/j.atech.2025.101004 · Cited by 3 · see citing works
Leveraging Domain-Specific Word Embedding and Hate Concepts in Hate Speech Detection
Wu, X., Wu, H., & He, P. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650500 · Cited by 4 · see citing works
Chen, X., Chen, Y., & Yin, G. (2025) · Decision Support Systems · doi:10.1016/j.dss.2025.114409 · Cited by 3 · see citing works
Sánchez, A. M. S., Ruiz‐Muñoz, D., & Sánchez‐Sánchez, F. J. (2023) · Cuadernos info · doi:10.7764/cdi.56.60093 · Cited by 3 · see citing works
Machine Learning based Intelligent Cyberbullying Avoidance System
Dhanalakshmi, D., Rani, N. D., Pendam, K., Hariharan, S., Kukreja, V., & Jayakshata, P. (2023) · International Conference on Sustainable Computing and Smart Systems (ICSCSS) · doi:10.1109/icscss57650.2023.10169376 · Cited by 3 · see citing works
Arif, M., Rahman, M. M., Alam, M. G. R., & Akhtaruzzaman, M. (2024) · MIST INTERNATIONAL JOURNAL OF SCIENCE AND TECHNOLOGY · doi:10.47981/j.mijst.12(02)2024.466(39-52 · Cited by 2 · see citing works
Sentiment-Aware Summary Generation for User Reviews Using Deep Learning Models
Danyal, M. M., Khalid, A., Khan, S. S., Ullah, S., Jan, H.-C., & Khan, D. (2025) · VFAST Transactions on Software Engineering · doi:10.21015/vtse.v13i3.2240 · Cited by 2 · see citing works
Cyberbullying Detection on Social Media: A Brief Survey
Hussein, F. N. A., & Aleqabie, H. J. (2023) · International Conference on Advanced Computer Applications (ACA) · doi:10.1109/aca57612.2023.10346758 · Cited by 3 · see citing works
Assessing English Language Writing and Readability Skills using Long Short-Term Memory Model
Mayahi, A. J., & Alshatti, E. N. (2023) · Computer Applications & Technological Solutions (CATS) · doi:10.1109/cats58046.2023.10424106 · Cited by 2 · see citing works
Anti-Cyberbullying System Using AI in Social Media
Hosanna, M., & Suguna, S. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-6681-9_1 · Cited by 2 · see citing works
Improving Cyberbullying Detection Accuracy with Advanced Machine Learning Models
Dhakshnamoorthi, T., Giriraj, M., Gokul, S., & Vanitha, K. (2024) · International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS) · doi:10.1109/csitss64042.2024.10817024 · Cited by 2 · see citing works
A Comprehensive Review of Machine Learning and Deep Learning Techniques for Cyberbullying Detection
Jazzar, M., & Duridi, T. (2025) · Smart innovation, systems and technologies · doi:10.1007/978-981-96-2182-8_1 · Cited by 3 · see citing works
Deep Learning Techniques For Spamming And Cyberbullying Detection
Meenakshi, M., Babu, P. S., & Hemamalini, V. (2023) · International Conference on Networking and Communications (ICNWC) · doi:10.1109/icnwc57852.2023.10127460 · Cited by 2 · see citing works
Cyberbullying Detection Using BiLSTM Model
Lekshmi, M. S., Shaji, A. M., & Amrita, S. K. (2024) · Signals and communication technology · doi:10.1007/978-3-031-47942-7_29 · Cited by 2 · see citing works
Saim, M., Rizvi, R. M., & Khan, M. K. (2023) · International Conference on Recent Advances in Science and Engineering Technology (ICRASET) · doi:10.1109/icraset59632.2023.10419938 · Cited by 1 · see citing works
Cyberbullying Detection in Group Chat Applications- A Review
Bhardwaj, R., Billade, A., Chenna, S., Deshpande, A., & Arthamwar, V. (2024) · International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS) · doi:10.1109/icicnis64247.2024.10823118 · Cited by 1 · see citing works
Mahdi, M. A., Arshed, M. A., & Mumtaz, S. (2026) · Mathematics · doi:10.3390/math14010170 · Cited by 1 · see citing works
Multi-class Offensive Language Detection in Roman Urdu
Ayesha, R. L. A. S., Ali, S. ع. A., Inayat, U., & Mahmood, S. (2025) · VFAST Transactions on Software Engineering · doi:10.21015/vtse.v13i4.2251 · Cited by 1 · see citing works
Zhong, Y., Zhang, Z.-W., Ding, J.-H., Sun, Y., Zheng, Y., Yu, Y.-F., Guan, Z.-C., Su, S.-Z., Wu, T., Gong, Y.-M., et al. (2026) · Molecular Psychiatry · doi:10.1038/s41380-026-03605-4 · Cited by 1 · see citing works
Detecting E-Bullying in Social Media Platform with Stacked BiLSTM Approach
Thilakaraj, D., Ajay, G. S., & Namboothiri, L. V. (2024) · International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN) · doi:10.1109/icstsn61422.2024.10671386 · Cited by 1 · see citing works
Cyberbullying Detection and Severity Classification Using Bi-LSTM
Devi, K. N., Rajasekar, V., Jayanthi, P., Balasubramani, K., Kandasamy, K., & Gowrisankar, K. (2024) · International Conference on Communication and Electronics Systems (ICCES) · doi:10.1109/icces63552.2024.10859875 · Cited by 1 · see citing works
Berbery, K., Samrouth, K., Bakir, N., & Dawood, M. (2025) · International Conference on Smart Applications, Communications and Networking (SmartNets) · doi:10.1109/smartnets65254.2025.11106890 · Cited by 1 · see citing works
PFB (2021) · Pesquisa Florestal Brasileira · doi:10.4336/2021.pfb.41e202102240
Jehad, H., Abu-Alhaija, M., & Tarawneh, H. (2023) · Research Square · doi:10.21203/rs.3.rs-3427084/v1
Nethmini, S. A. H., & Weerakoon, C. (2023) · SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) · doi:10.1109/slaai-icai59257.2023.10365011
Past, Present, and Future of Automatic Cyberbullying Detection Research
Ptaszynski, M. E. (2023) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.35.3_38
Detecting Cyberbullying through social media: A Deep Learning Approach
Kadiri, P., Arjun, U., Sravani, N., Jyothi, N. S., Mahesh, P. A., & Naik, S. J. (2024) · International Conference on Advances in Modern Age Technologies for Health and Engineering Science (AMATHE) · doi:10.1109/amathe61652.2024.10582256
Reddy, K. B., & Ramkumar, G. (2024) · AIP conference proceedings · doi:10.1063/5.0227905
Hamim, H., Uddin, K. M. M., Mim, M. N. T., Mostafiz, R., & Based, M. A. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-3937-0_36
Unified Classifier Stacking: A Comprehensive Ensemble Approach to Cyberbullying Detection
Kumar, P., Uppal, G., Upadhyay, R., Gupta, R., Dabral, O., Dwivedy, N., & Kumar, B. (2024) · IEEE Conference on Information and Communication Technology (CICT) · doi:10.1109/cict64037.2024.10899548
Cyberbullying Detection using Deep Learning Algorithms
Devi, K. N., Jayanthi, P., Rajasekar, V., Rathinasamy, R., Dhandapani, P., & Rasil Risam, F. (2025) · International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT) · doi:10.1109/idciot64235.2025.10915097
Redefining Safety Nets: Text Detection solution for Cyberbullying Eradication
Rao, K. B., Suvarnamukhi, B., Famila, S., Hariharan, S., Thava Vinu, A., & Vasantha, S. (2024) · International Conference on Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC) · doi:10.1109/icdscnc62492.2024.10939179
DETECTION OF HATE SPEECH ON SOCIAL MEDIA UTILIZING MACHINE LEARNING
Zhidebayeva, A., Akhmetova, S., Mamikov, S., Kerimbekov, M., Aldeshov, S., & Шаймерденова, Г. (2025) · Scientific Journal of Astana IT University · doi:10.37943/22sksg8575
Vijayakumar, P. J., Das, M. S., Bansod, P. J., Kumar, S., Akshaya, R., & Praveena, S. (2025) · Global Conference in Emerging Technology (GINOTECH) · doi:10.1109/ginotech63460.2025.11076700
Cyberbullying Detection in Assamese Social Media: A Comparative Study of BiGRU and CNN Models
Dutta, S., Chutia, T., & Baruah, N. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-5010-1_5
User-Centric Toxicity Detection with Multi-Modal Spiking Neural Networks and Policy Optimization
Sayad, I. A. E., & Nakshabandi, M. A. (2025) · International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) · doi:10.1109/acdsa65407.2025.11166052
Wasid, M., Amer, A. Y. A., Ahmad, S., & Mustafa, J. (2025) · International Journal of Reliability Quality and Safety Engineering · doi:10.1142/s021853932550038x
Nayak, I., Ashok, K., Sridevi, B., Immanuel, G., Namig, N. A., & Shunmugasundaram, M. (2025) · World Conference on Communication & Computing (WCONF) · doi:10.1109/wconf64849.2025.11233760
Fan, W. (2025) · Scientific Reports · doi:10.1038/s41598-025-26024-x
Detection of Cyber Bullying Using Machine Learning
Kalyani, N. (2025) · International Journal of Innovative Research in Information Security · doi:10.26562/ijiris.2025.v1110.11
Li, B., Tan, J., & Ma, L. (2025) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei68103.2025.11259380
Ramana, C. V., Sumathy, V., Vullam, N., Prasanthi, G., Lakshmanarao, A., & Rao, P. V. (2025) · International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS) · doi:10.1109/icicnis66685.2025.11315743
Bhuvaneswari, S., Sandeep Kumar, M., Keerthika.MP, Charuroopa.S, R, R. S., & Sudha.S, K. (2025) · International Conference of Advances in Engineering and Computing Technologies for Sustainable Development (AECTSD) · doi:10.1109/aectsd65988.2025.11411429
Mounica, V., & Mohan, A. K. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115991
Cyberbullying Detection in Social Media: Comparing ML Models with the Render Forest Ensemble
Sharma, D., Mehndiratta, V., & Mathew, A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-25958-5_35
Sustainable AI solutions: A review of green technology for hate speech detection in vídeos
Setyawan, R. A., Surjono, H. D., & Wardani, R. (2026) · Multidisciplinary Reviews · doi:10.31893/multirev.2027014
Loseto, G., Gramegna, F., Pinto, A., Scioscia, F., & Ruta, M. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02204-0
КИБЕРБУЛЛИНГТІ АНЫҚТАУДА ТЕРЕҢ ОҚЫТУ ӘДІСТЕРІНІҢ ТИІМДІЛІГІН ТАЛДАУ
Abdrakhmanov, R., Kerimkhan, B., А.С., К., Iskakov, T. B., & Уксикбаев, Е. З. (2026) · Научный журнал "Вестник Актюбинского регионального университета имени К Жубанова" · doi:10.70239/arsu.2026.t85.n3.03
DENG, S., Shaari, A. H., Shaari, A. A. H., & Ismail, I. N. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1963458
- Authors
- Dewani, A., Memon, M. A., & Bhatti, S.
- Affiliations
- Mehran University of Engineering and Technology (PK)
- Corresponding author
- Dewani, Amirita
- Venue
- Journal of Big Data
- Published
- 2021-12
- Volume
- 8
- Issue
- 1
- Pages
- 160-160
- ISSN
- 2196-1115 (ISSN-L)
- PMC
- PMC8693595
- OpenAlex
- W4200127519
- Cited by
- 91
- References
- 23
- Citations by year
- 2026: 8 · 2025: 27 · 2024: 29 · 2023: 21 · 2022: 5 · 2021: 1
- FWCI
- 4.93
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal Of Big Data (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Greater South Information System
- Funders
- University of Engineering and Technology, Lahore
- Mehran University of Engineering and Technology
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- cyberbullying detection, Roman Urdu, hate speech, slang dictionaries, text preprocessing, social media text, LSTM-RNN, BiLSTM, convolutional neural networks, low-resource languages, microtext
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
