A Multichannel Deep Learning Framework for Cyberbullying Detection on Social Media
Open accessPreprint — published as 10.3390/electronics10212664
Abstract
Online social networks (OSNs) play an integral role in facilitating social interaction; however, these social networks increase antisocial behavior, such as cyberbullying, hate speech, and trolling. Aggression or hate speech that takes place through short message service (SMS) or the Internet (e.g., in social media platforms) is known as cyberbullying. Therefore, automatic detection utilizing natural language processing (NLP) is a necessary first step that helps prevent cyberbullying. This research proposes an automatic cyberbullying method to detect aggressive behavior using a consolidated deep learning model. This technique utilizes multichannel deep learning based on three models, namely, the bidirectional gated recurrent unit (BiGRU), transformer block, and convolutional neural network (CNN), to classify Twitter comments into two categories: aggressive and not aggressive. Three well-known hate speech datasets were combined to evaluate the performance of the proposed method. The proposed method achieved promising results. The accuracy of the proposed method was approximately 88%.
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Attention-Based Bi-LSTM (AB-LSTM) is an effective neural network model for detecting cyberbullying on social media sites such as Twitter.
“Attention-Based Bi-LSTM (AB-LSTM) is also an effective neural network model for detecting cyberbullying on social media sites such as Twitter [115,121,122].”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [115]
The Related Work section describes a BiLSTM with an attention layer among architectures evaluated for cyberbullying detection on datasets including Twitter, and says the broader framework overcame some limitations experimentally. It does not report a separate effectiveness result for attention-based BiLSTM; the paper’s own positive results concern a different, multichannel model (Abstract; Conclusions). (LLM, conf=0.78)
Experimental — accuracy not yet measuredTransformers are deep learning models based on attention that have shown effectiveness in detecting cyberbullying.
“Transformers are deep learning models based on attention that have shown effectiveness in detecting cyberbullying [113-115].”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [115]
Alotaibi et al. [115] found their multichannel deep learning framework outperformed individual models with an accuracy score of 88% on Twitter comments.
“Alotaibi et al. [115] proposed a multichannel deep learning framework where they proposed a combination of transformer block, Bi-GRU, and CNN to detect cyberbullying in Twitter comments where the model outperforms the individual model with an accuracy score of 88%.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [115]
Several studies present performance evaluation of different hybrid deep-learning-based approaches.
“In the literature, there are several studies that also present the performance evaluation of different hybrid deep-learning-based approaches [66,115,151,152].”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [115]
Several studies have been conducted on automatic identification of cyberbullying using different independent ML techniques.
“Several studies [21,64,115,134] have been conducted on the automatic identification of cyberbullying by using different independent ML techniques.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [115]
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BackgroundMohiuddin, G. M., Sayeed, M. S., & Ong, L. Y. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00577-2 · Cited by 9 · see citing works
“A multichannel framework combining transformer blocks, Bi-GRU, and CNN was proposed by Alotaibi et al. [122] to detect cyberbullying in Twitter comments, achieving 88% accuracy and surpassing individual models.”
Prathaban, B. P., Vaishnavi, T., Ramesh, A. P., Sujaritha, M., Rama, R. S., & Jeffin Gracewell, J. (2024) · International Journal of Information Technology · doi:10.1007/s41870-024-02350-7 · Cited by 3 · see citing works
“Although cyberbullying, which is solely psychological and emotional, traditional harassment can result in physical as well as mental damage [8].”
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
“By combining transformer blocks, convolutional neural networks (CNN), and BiGRU, [13] developed a powerful method for classifying Twitter comments as aggressive or not.”
MethodAmbareen, K., & Sundaram, S. M. (2023) · SN Computer Science · doi:10.1007/s42979-023-02301-2 · Cited by 6 · see citing works
Abebaw, Z., Rauber, A., & Atnafu, S. (2022) · Revue d'Intelligence Artificielle · doi:10.18280/ria.360201 · Cited by 6
“Alotaibi et al. [32] developed a multichannel deep learning model that combines three networks, the bidirectional gated recurrent unit, transformer block, and convolutional neural network, to classify 55,788 Twitter comments into aggressive and non-aggressive classes split into 75% training and 25%…”
BackgroundAlsuwaylimi, A. A., & Alenezi, Z. S. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.061674 · Cited by 3 · see citing works
“Similarly, using a deep learning approach, authors of [9] developed a Multichannel Deep Learning Framework for Cyberbullying Detection on Twitter, which achieved 88% accuracy.”
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
“The objective of cyberbullying detection (for instance, from a tweet) is to pre-process text and extract the pertinent information in order to create a machine-learning algorithm that will comprehend and categorise content, similar to other NLP tasks [7].”
BackgroundMethodInfluentialThe Unappreciated Role of Intent in Algorithmic Moderation of Social Media Content
Wang, X., Koneru, S., Venkit, P., et al. (2024) · arXiv.org · doi:10.48550/arxiv.2405.11030 · Cited by 4
Explainable AI for Reliable Detection of Cyberbullying
Gongane, V. U., Munot, M. V., & Anuse, A. D. (2023) · IEEE Pune Section International Conference (PuneCon) · doi:10.1109/punecon58714.2023.10450132 · Cited by 5 · see citing works
An Ensemble Approach for Cyber Bullying: Text Messages and Images
Bai, Z. S., & Malempati, S. (2023) · Revue d intelligence artificielle · doi:10.18280/ria.370122 · Cited by 5 · see citing works
“Many researchers are trying to develop an automated cyber-bullying model to detect and prevent this message [6].”
BackgroundShibly, F. H. A., Sharma, U., & Naleer, H. M. M. (2022) · Journal of Pharmaceutical Negative Results · doi:10.47750/pnr.2022.13.s03.072 · Cited by 2 · see citing works
Kaur, N., Singh, P., Singh, K., Khan, J., Hussain, D., Gu, Y. H., Aljuaidi, R., & Rajkhan, N. W. (2026) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2026.1851416 · Cited by 3 · see citing works
Enhanced Arabic-language cyberbullying detection: deep embedding and transformer (BERT) approaches
Aljohani, E. J., & Yafooz, W. M. S. (2025) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v14.i3.pp2258-2269 · Cited by 3 · see citing works
“Social media users usually use pseudonyms in order to hinder efforts to monitor their behavior [6].”
Cyberbullying Detection and Prevention On Social Media
Mr, M. (2024) · International Scientific Journal of Engineering and Management · doi:10.55041/isjem01583 · Cited by 3
“However, their method achieved only an accuracy of 70 percent and the size of the used dataset is relatively small [2].”
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
“A lot of research work was conducted on explicit speech identification [9].”
Illuminating the Shadow: Building an Advanced Semantic Cyberbullying Detection System
Sad-Houari, N., & Sad-Houari, K. (2025) · Journal of Intelligent & Fuzzy Systems · doi:10.1177/18758967251388262 · Cited by 2 · see citing works
“The latter is inspired by machine learning, data mining, automatic translation, and natural language processing (Alotaibi et al., 2021; Elsafoury et al., 2021; Eronen et al., 2022; Hasan et al., 2023), and makes it possible to analyze large-scale data in order to extract valuable information about…”
Research on automatic error-checking in English short text translation by a neural network algorithm
Guo, L. (2024) · Artificial Life and Robotics · doi:10.1007/s10015-024-00952-9 · Cited by 3 · see citing works
“Alotaibi et al. [7] integrated BiGRU, Transformer, and convolutional neural network to automatically detect cyber-bullying language in online social media texts, achieving an accuracy rate of 88% on three datasets that distinguish between offensive and non-offensive Twitter comments.”
From Tweets to Tolerance: Empowering Cyberbullying Detection with Deep Learning Models
Asfand-e-yar, M., Aftab, S., Hashir, Q., & Talha, T. (2023) · International Conference on Frontiers of Information Technology (FIT) · doi:10.1109/fit60620.2023.00050 · Cited by 3 · see citing works
“Other techniques in [8], [9], and [10] used LSTM with CNN or GRU to classify cyberbullying on different datasets.”
MethodChow, D. V., Natania, F., Nathanael, O. T., Setiawan, K. E., & Hasani, M. F. (2023) · International Conference on Cybernetics and Intelligent System (ICORIS) · doi:10.1109/icoris60118.2023.10352294 · Cited by 1 · see citing works
“By combining three deep learning architectures (a multichannel architecture made up of BiGRU, a transformer block, and CNN models), Munif Alotaibi et al. proposed a new method for detecting cyberbullying that outperformed state-of-the-art techniques [8].”
MethodCan Machine Learning Really Detect Cyberbullying?
Pokhun, L., & Chuttur, M. Y. (2023) · International Journal of Bullying Prevention · doi:10.1007/s42380-023-00191-9 · Cited by 4 · see citing works
“The transformer architecture was used in another study by Alotaibi et al. (2021).”
Classification of Text, Image and Audio Messages Used for Cyberbulling on Social Medias
Idrizi, E., & Hamiti, M. (2023) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.23919/mipro57284.2023.10159835 · Cited by 5 · see citing works
A New Model for a Secure Social Media Application
Riad, K., & Elhoseny, M. (2022) · Applied Sciences · doi:10.3390/app12136375 · Cited by 1 · see citing works
Akwa, G. (2026) · AI and Ethics · doi:10.1007/s43681-026-01054-x · Cited by 1 · see citing works
Contextual Cyberbullying Detection Using Emotion Enhanced Bystander Modeling
Devi, K., Vanaja, T., Kavitha, S., et al. (2025) · 2025 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT) · doi:10.1109/iconscept66142.2025.11436665 · Cited by 1
Advanced social media crime prevention via deep learning and cryptographic data encryption
Alserhani, F. (2025) · The Computer Journal · doi:10.1093/comjnl/bxaf028 · Cited by 1 · see citing works
NLP Machine Learning for Online Harassment Detection on Social Media
Daruvuru, P., Reddy, P. C. K., Kavitha, G., & Amshavalli, R. S. (2025) · International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) · doi:10.1109/icaiss61471.2025.11041971 · Cited by 1 · see citing works
“The work in [18] presented a new deep learning framework for detecting cyberbullying across different social media platforms.”
Alanazi, B. A., & Lin, C.-T. (2025) · international journal of engineering trends and technology · doi:10.14445/22315381/ijett-v73i3p106 · Cited by 1
“In 2021 [72], an automatic cyberbullying detection method based on a combined deep-learning model was proposed to identify aggressive behaviour.”
Deep Learning Techniques for Cyber Bullying Detection in Social Networks
Selvi, V., Rappai, S., Kalpana, D. Y., Sabaresan, V., Hegde, N. P., & Sathya, V. (2025) · International Conference on Automation and Computation (AUTOCOM) · doi:10.1109/autocom64127.2025.10957657 · Cited by 1 · see citing works
“Either a string cleansing system or a stripping technique is the tokenizer used in tokenizing [1].”
Harnessing RNN for Enhanced Hate Speech Detection in Social Media
Rajani, C., Srinadh, T., Chand, K., Kumar, B. A., Sreenivasu, S. V. N., & Venkatareddy, D. (2024) · International Conference on Integrated Intelligence and Communication Systems (ICIICS) · doi:10.1109/iciics63763.2024.10859351 · Cited by 1 · see citing works
“Alotaibi et al. [12] proposed a multiple channel deep learning model that incorporates the integration of various types of data, such as textual information with contextual information, for enhanced accuracy in detection.”
Saim, M., & Rizvi, R. M. (2023) · DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIHEI) · doi:10.1109/idicaiei58380.2023.10406622 · Cited by 2 · see citing works
A Survey on Cyberbullying Classification and Detection
Perumal, S. V., & Pamila, J. C. M. J. (2023) · Journal of Information Technology and Digital World · doi:10.36548/jitdw.2023.2.001 · Cited by 1 · see citing works
Cyberbullying and Suicide Ideation Detection via Hybrid Machine Learning Model
Mishra, A. S., Srivastava, A., Modi, A. R., & Pandey, A. (2023) · International Conference on Signal Processing and Integrated Networks (SPIN) · doi:10.1109/spin57001.2023.10117371 · Cited by 2 · see citing works
“The current state-of-the-art research emphases on the use of deep learning and machine learning models for different NLP problems [3].”
BackgroundAnalysing Machine Learning Techniques for Cyberbullying Detection: A Review Study
Aziz, S., Usman, M., Azam, A., Ahmad, F., Bilal, M., & Cheema, A. A. (2022) · International Conference on Emerging Technologies · doi:10.1109/icet56601.2022.10004667 · Cited by 3 · see citing works
“…comments, 970 sessions [17] Formspring (QA forum), Wikipedia 12773 QA, 115864 Comments [18] Vine, Instagram 970 sessions, 2218 comments [19] Myspace 381,000 posts [20] Twitter 35273 Arabic Tweets [21] Twitter 55,788 tweets [22] Twitter 1762 Tweets and deep learning solutions to the said problems.”
BackgroundAdvances in AI-Based Methods for Cyberbullying Detection on the X Platform: A Systematic Review
Al-Omari, K. H., Al-Ghamdi, R. S., Al-Suhaimi, S. H. A.-S., et al. (2026) · International Journal of Business and Information Communication Technologies · doi:10.68092/ijbict.01.01.04
TSAN-CB: a token-salience aware transformer framework for context-rich cyberbullying detection
Thirunavukkarasan, M., & Selvanambi, R. (2026) · Engineering Research Express · doi:10.1088/2631-8695/ae9bfc
“This drawback is particularly pronounced in datasets with sarcasm or indirect harassment, where less than 4% of words are abusive [17, 18].”
Stacking Ensemble vs IndoBERT for Indonesian Cyberbullying Detection
Justin, J., Howen, K., Wijaya, A., Jabar, B. A., & Halim, R. (2026) · International Conference on Information Management and Technology (ICIMTech) · doi:10.1109/icimtech202671078.2026.11699022
Rodriguez-Vega, M., Rosa-Remedios, C. B., & Caballero-Gil, P. (2026) · Online Soc. Networks Media · doi:10.1016/j.osnem.2026.100346
Sathea Sree, S., & Nalini Joseph, L. (2026) · International Research Journal of Multidisciplinary Technovation · doi:10.54392/irjmt26315
Si, X., Pan, Y., & Kang, H. (2026) · Journal of Intelligent & Fuzzy Systems · doi:10.1177/18758967261435710
“…fosters antisocial behaviors like cyberbullying—aggressive, hateful, or insulting communication spread via online platforms, manifesting as personal attacks, sarcasm, and group bullying that endangers individual mental health and social stability (Agbaje and Afolabi, 2024; Alotaibi et al., 2021).”
Sathea Sree, S., & Nalini Joseph, L. (2026) · International Research Journal of Multidisciplinary Technovation · doi:10.54392/irjmt26216
“There were numerous new techniques, such as CNN, BiLSTM, BiGRU, Transformers, and ensemble methods that have all been applied [11, 15].”
Idrizi, E., Hamiti, M., & Imeri-Saiti, D. (2026) · International journal of education & well-being. · doi:10.62416/ijwb-79
An Efficient Hybrid Deep Learning Framework for Cyberbullying Detection
Durgekar, S. R., Hegde, R., & Hegde, S. K. (2026) · 2026 Second International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI) · doi:10.1109/icmsci67830.2026.11469207
“Alotaibi et al. [11] trained a deep multichannel architecture to detect cyberbullying on social media, targeting the limitations of single-model architectures in extracting various patterns of linguistic usage.”
Ye, J., Wang, X.-J., Wang, X.-J., et al. (2025) · 2025 Tenth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) · doi:10.1109/iconstem65670.2025.11374823
“In [11], authors want to provide a thorough overview of the topic by talking about both the basics of taxonomy and the latest developments in identifying hate speech on the internet.”
Raymond, K., Ben-Rabah, N., Leblanc, A., et al. (2025) · IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat67162.2025.00081
“Models range from traditional methods like Random Forest ([2],[6]) and XGBoost ([2],[4]) to deep learning (CNN ([1],[3],[5],[6]), LSTM ([3],[5],[6],[9])), transformer-based models (BERT ([1],[3],[4],[9])), and ensemble approaches ([2],[4],[8]).”
Thoyyibah, T. (2025) · International Conference on Computing and Applied Informatics (ICCAI) · doi:10.1109/iccai65301.2025.11279387
“Machine learning is widely applied in social media, psychology, security, network, 4D, COVID, speech, location, object detection, augmented reality, and YouTube videos [17] [18][19][20][21] [22][23][24][25][26][27][28][29].”
Cyberbullying Detection: A Comparative Evaluation of Learning Models on a Unified Public Dataset
Ghanem, D., Mansour, D., AlSayeh, R., Amari, Z., Hamzah, A., & Maabreh, M. (2025) · International Conference on Cybersecurity and AI-Based Systems (Cyber-AI) · doi:10.1109/cyber-ai66431.2025.11233349
“A multichannel deep learning framework for cyberbullying detection on social media was developed in [10].”
Optimizing Cyberbullying Detection in Tweets with an Ensemble Learning Framework
Singh, M. (2025) · Journal of Informatics Education and Research · doi:10.52783/jier.v5i3.3194
“[14] created an advanced deep learning model that combines BiGRU, transformer block, and CNN architectures to accurately categorize tweets as either aggressive or non-aggressive.”
Cyberbullying Detection on Twitter: A Comparison of One-Step vs. Two-Step Classification Approaches
Halim, D. E. S., Tandoko, F. A., Wulandhari, L. A., & Nabiilah, G. Z. (2025) · IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) · doi:10.1109/iaict65714.2025.11101479
“Bayesian and transfer learning models, including CNN and Naïve Bayes, excel in complex detection tasks, achieving up to 87.99% accuracy in hybrid configurations [31].”
InfluentialA Cross-Layer-Routing Protocol with Malicious node detection using BiRNN in WSN
S.Saritha (2025) · Communications on Applied Nonlinear Analysis · doi:10.52783/cana.v32.3824
“The network that is bidirectional in rather than eliminating individual components, the model begins with a spatial dropout layer that eliminates entire feature maps [24].”
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
“The study [12] integrated three models into one: Bidirectional Gated Recurrent Unit (BiGRU), Transformer Block, and Convolutional Neural Network (CNN).”
Detection of Cyberbullying Incidents on the X Social Network
Elhan, A., Chuanito, D., Lucky, H., & Suhartono, D. (2024) · International Conference on Technology Innovation and Its Applications (ICTIIA) · doi:10.1109/ictiia61827.2024.10761832
“Furthermore, a complex deep learning framework for cyberbullying detection is proposed in A Multichannel Deep Learning Framework for Cyberbullying Detection on Social Media [26], which makes use of many data channels to improve detection robustness and accuracy.”
Utilizing Machine Learning for Detecting Cyber Bullying in Social Media
Yousha, S., Memon, S. A., & Talpur, S. (2023) · International Journal of Innovations in Science and Technology · doi:10.33411/ijist/598
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
“In [11], the authors presented a multi-channel architecture approach using three deep learning models: trans-former block, bidirectional GRU, and CNN.”
MethodCuzzocrea, A., Gallo, C., Akter, M. S., & Shahriar, H. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.09.153
- Authors
- Alotaibi, M., Alotaibi, B., & Razaque, A.
- Affiliations
- Shaqra University (SA)
- University of Tabuk (SA)
- International Information Technologies University (KZ)
- Corresponding author
- Alotaibi, Munif
- Alotaibi, Bandar
- Razaque, Abdul
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2021-10-05
- Volume
- 10
- Issue
- 21
- Pages
- 2664-2664
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W3208463727
- Cited by
- 94
- References
- 34
- Citations by year
- 2026: 13 · 2025: 22 · 2024: 16 · 2023: 28 · 2022: 13 · 2021: 2
- FWCI
- 5.19
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Electronics (publishedVersion, cc-by)
- Preprints.org (acceptedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- 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, social media, Twitter comments, hate speech, aggression, bidirectional gated recurrent unit, transformer models, convolutional neural networks, text classification
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