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DEA-RNN: A Hybrid Deep Learning Approach for Cyberbullying Detection in Twitter Social Media Platform
Open accessAbstract
Cyberbullying (CB) has become increasingly prevalent in social media platforms. With the popularity and widespread use of social media by individuals of all ages, it is vital to make social media platforms safer from cyberbullying. This paper presents a hybrid deep learning model, called DEA-RNN, to detect CB on Twitter social media network. The proposed DEA-RNN model combines Elman type Recurrent Neural Networks (RNN) with an optimized Dolphin Echolocation Algorithm (DEA) for fine-tuning the Elman RNN’s parameters and reducing training time. We evaluated DEA-RNN thoroughly utilizing a dataset of 10000 tweets and compared its performance to those of state-of-the-art algorithms such as Bi-directional long short term memory (Bi-LSTM), RNN, SVM, Multinomial Naive Bayes (MNB), Random Forests (RF). The experimental results show that DEA-RNN was found to be superior in all the scenarios. It outperformed the considered existing approaches in detecting CB on Twitter platform. DEA-RNN was more efficient in scenario 3, where it has achieved an average of 90.45% accuracy, 89.52% precision, 88.98% recall, 89.25% F1-score, and 90.94% specificity.
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“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 [151]
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1 retracted
RETRACTED ARTICLE: Feature selection techniques in the context of big data: taxonomy and analysis
Abdulwahab, H. M., Ajitha, S., & Saif, M. A. N. (2022) · Applied Intelligence · doi:10.1007/s10489-021-03118-3 · Cited by 73 · cited 1×
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“Whereas the other keywords such as ban, kill, die, evil, hate, attack, terrorist, threat, racism, black, Muslim, Islam, and Islamic were suggested in [39].”
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“In India, for example, 14 percent of all harassment occurs on Facebook and Twitter, with 37 percent of these incidents involving youngsters [1].”
BackgroundUsing Machine Learning to Detect Cyberbullying
Reynolds, K., Kontostathis, A., & Edwards, L. (2011) · doi:10.1109/icmla.2011.152 · Cited by 462 · cited 1×
“The oversampling process is performed by replicating cyberbullying samples many times to balance the dataset as used in [15], [16].”
MethodEffective hate-speech detection in Twitter data using recurrent neural networks
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Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms
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“The oversampling process is performed by replicating cyberbullying samples many times to balance the dataset as used in [15], [16].”
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“As cyberbullying is becoming a prevalent problem in Twitter, the detection of cyberbullying events from tweets and provisioning preventive measures are the primary tasks in battling cyberbullying threats [5].”
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“[28] investigated cyberbullying identification using different ML classifiers such as RF, Naïve Bayes (NB), and SVM based on various extracted features from Twitter such as (tweet content, activity, network, and user).”
MethodLearning from Bullying Traces in Social Media
Xu, J., Jun, K.-S., Zhu, X., & Bellmore, A. D. (2012) · Cited by 332
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Cited by
Sorted by citations
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
“Murshed et al. [151] proposed a hybrid method named DEA-RNN that combines Elman-type Recurrent Neural Networks (RNN) with an optimized Dolphin Echolocation Algorithm (DEA) that optimizes the training time to detect cyberbullying on Twitter.”
BackgroundMethodShort text topic modelling approaches in the context of big data: taxonomy, survey, and analysis
Murshed, B. A. H., Mallappa, S., Abawajy, J., Saif, M. A. N., Al-ariki, H. D. E., & Abdulwahab, H. M. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10254-w · Cited by 87
Self-citationSession-based cyberbullying detection in social media: A survey
Yi, P., & Zubiaga, A. (2023) · Online Social Networks and Media · doi:10.1016/j.osnem.2023.100250 · Cited by 55
Ejaz, N., Razi, F. D., & Choudhury, S. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.108123 · Cited by 57
An effective approach for Arabic document classification using machine learning
Muaad, A. Y. M., Kumar, G. H., Hanumanthappa, J., Benifa, J. V. B., Mourya, M. N., Chola, C., Pramodha, M., & Bhairava, R. (2022) · Global Transitions Proceedings · doi:10.1016/j.gltp.2022.03.003 · Cited by 43
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
“Large social media networks such as Twitter are prone to cyberbullying because their widespread popularity can provide anonymity to abusers [2].”
BackgroundResultFAEO-ECNN: cyberbullying detection in social media platforms using topic modelling and deep learning
Murshed, B. A. H., Suresha, Abawajy, J., Saif, M. A. N., Abdulwahab, H. M., & Ghanem, F. A. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15372-3 · Cited by 39
Self-citationChild Safety and Protection in the Online Gaming Ecosystem
Faraz, A., Mounsef, J., Raza, A., & Willis, S. (2022) · IEEE Access · doi:10.1109/access.2022.3218415 · Cited by 50
“It is also observed that the focus of research on detecting predatory behavior is on chat logs pertaining to social media’s chatting platforms [123], [124], [125], [126], [127].”
BackgroundDeep learning approach to detect cyberbullying on twitter
Oğuz, Ç., & Yağanoğlu, M. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19869-3 · Cited by 38
Enhancing Big Social Media Data Quality for Use in Short-Text Topic Modeling
Murshed, B. A. H., Abawajy, J., Mallappa, S., Saif, M. A. N., Al-Ghuribi, S. M., & Ghanem, F. A. (2022) · IEEE Access · doi:10.1109/access.2022.3211396 · Cited by 29
“This dataset is expanded to the collected dataset utilized in [14] and classified it into five classes: Not-bullying, sexism, racism, aggressive, and insult.”
BackgroundMethodSelf-citationLi, T., Zeng, Z., Li, Q., & Sun, S. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103651 · Cited by 29
ProTect: a hybrid deep learning model for proactive detection of cyberbullying on social media
Harshitha, T., Prabu, M., Suganya, E., Sountharrajan, S., Bavirisetti, D. P., Gadde, N., & Uppu, L. S. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1269366 · Cited by 28
A comprehensive review of cyberbullying-related content classification in online social media
Teng, T. H., Varathan, K. D., & Crestani, F. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122644 · Cited by 29
Mali, M. K., Pawar, R. R., Shinde, S. A., Kale, S. D., Mulik, S. V., Jagtap, A. A., Tambewagh, P. A., & Rajput, P. U. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125641 · Cited by 24
Daniel, R., Murthy, T. S., Kumari, C. D. V. P., Lydia, E. L., Ishak, M. K., Hadjouni, M., & Mostafa, S. M. (2023) · IEEE Access · doi:10.1109/access.2023.3326948 · Cited by 21
“Murshed et al. [11] presented a hybrid DL system termed DEA-RNN for identifying CB on Twitter social media networks.”
MethodKhalid, M., Raza, A., Younas, F., Rustam, F., Villar, M. G., Ashraf, I., & Akhtar, A. (2024) · IEEE Access · doi:10.1109/access.2024.3398582 · Cited by 19
Natural language processing for social science research: A comprehensive review
Hou, Y., & Huang, J. (2025) · Chinese Journal of Sociology · doi:10.1177/2057150x241306780 · Cited by 35
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
“The obtained results for the execution time were close to those mentioned in other studies, such as the study of [12].”
MethodResultCyberbullying Detection and Abuser Profile Identification on Social Media for Roman Urdu
Atif, A., Zafar, A., Wasim, M., Waheed, T., Ali, A., Ali, H., & Shah, Z. (2024) · IEEE Access · doi:10.1109/access.2024.3445288 · Cited by 16
“While quite a few deep learning and machine learning approaches have been developed for cyberbullying detection; these methods are primarily developed for the English language [2], [3], [4], [5], [6].”
Faraz, A., Ahsan, F., Mounsef, J., Karamitsos, I., & Kanavos, A. (2024) · Information · doi:10.3390/info15040233 · Cited by 20
Cyberbullying Detection on Social Media Using Machine Learning
Bokolo, B. G., & Liu, Q. (2023) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps57453.2023.10226114 · Cited by 14
Kehinde, T. O., Chung, S., & Olanrewaju, O. A. (2025) · IEEE Access · doi:10.1109/access.2025.3586417 · Cited by 13
“…industry, banking, logistics, transportation industry, supply chain, cyberbullying detection, farm and agriculture, power, healthcare, production and manufacturing, tourism, automobile, construction, insurance, transportation, and education [32], [33], [34], [35], [36], [37], [38].”
Detection of Cyberbullying Tweets in Twitter Media Using Random Forest Classification
Thangarasu, G., & Alla, K. R. (2023) · IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE) · doi:10.1109/iscaie57739.2023.10165118 · Cited by 13
“These challenges include: The incorporation of cutting-edge technology into the architecture of smart cities has led to the creation of new issues [1].”
BackgroundCNO-LSTM: A Chaotic Neural Oscillatory Long Short-Term Memory Model for Text Classification
Shi, N., Chen, Z., Chen, L., & Lee, R. (2022) · IEEE Access · doi:10.1109/access.2022.3228600 · Cited by 15
“For instance, rule-based matching from pattern recognition, machine learning also known as statistic learning method with supervised learning and unsupervised learning schemes, deep learning [23] modeling in AI and its hybrid alternatives [24], [25], [26].”
MethodAutomatic Short Text Summarization Techniques in Social Media Platforms
Ghanem, F. A., Padma, M. C., & Alkhatib, R. (2023) · Future Internet · doi:10.3390/fi15090311 · Cited by 17
MOBCSA: Multi-Objective Binary Cuckoo Search Algorithm for Features Selection in Bioinformatics
Abdulwahab, H. M., Ajitha, S., Saif, M. A. N., Murshed, B. A. H., & Ghanem, F. A. (2024) · IEEE Access · doi:10.1109/access.2024.3362228 · Cited by 12
“This algorithm utilized quantum bit representation to enhance exploration and exploitation of the classical WOA, enabling it to effectively search for optimal feature subsets [49], [50], [51], [52], [53], [54].”
MethodSelf-citationRecurrent neural network based multiclass cyber bullying classification
Sifath, S., Islam, T., Erfan, M., Dey, S. K., Islam, M., Samsuddoha, M., & Rahman, T. (2024) · Natural Language Processing Journal · doi:10.1016/j.nlp.2024.100111 · Cited by 14
Subhashree, K., & Kumar, S. M. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127555 · Cited by 11
A Novel Multimodal Hybrid Classifier Based Cyberbullying Detection for Social Media Platform
Suhas Bharadwaj, R., Kuzhalvaimozhi, S., & Vedavathi, N. (2023) · Lecture Notes in Networks and Systems · doi:10.1007/978-3-031-21438-7_57 · Cited by 8
Rasool, H. A., Al-Dolaimy, F., Hasan, F. F., Alsalamy, A., Saleem, M., Alkhayyat, A. H., & Sharma, M. (2023) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3609799 · Cited by 8
Detection of Indonesian Hate Speech on Twitter Using Hybrid CNN-RNN
Riyadi, S., Andriyani, A. D., Masyhur, A. M., Damarjati, C., & Solihin, M. I. (2023) · International Conference on Information Technology and Computing (ICITCOM) · doi:10.1109/icitcom60176.2023.10442041 · Cited by 8
“Although the suggested hybrid model outperformed other existing models in terms of performance, the compatibility of the DEA-RNN features declined as the input data was extended beyond the initial input [14].”
MethodArabic cyberbullying detection system using convolutional neural network and multi-head attention
Azzeh, M., Alhijawi, B., Tabbaza, A., Alabboshi, O., Hamdan, N., & Jaser, D. (2024) · International Journal of Speech Technology · doi:10.1007/s10772-024-10118-4 · Cited by 8
“Social media offers a modern paradigm for interpersonal communication, influencing the spectrum of communication channels and revolutionizing information dissemination and the formation of social relationships (Kumar & Sachdeva, 2022; Maity et al., 2022; Murshed et al., 2022).”
Balaji, P. G., Katariya, P. P., Sruthi, S., & Venugopalan, M. (2024) · International Conference on Knowledge Engineering and Communication Systems (ICKECS) · doi:10.1109/ickecs61492.2024.10616957 · Cited by 10
“An enhanced model similar to the hybrid CNN-BiLSTM model proposed in another research is the Dolphin Echolocation Algorithm (DEA) and Elman-type Recurrent Neural Networks (RNN) [16] model to optimise the neural network’s specifications and shorten training time.”
MethodMohiuddin, G. M., Sayeed, M. S., & Ong, L. Y. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00577-2 · Cited by 10
“Murshed et al. [124] introduced the DEA-RNN hybrid model, which combines RNN with the Dolphin echolocation algorithm (DEA) for detecting cyberbullying on Twitter, achieving 90.45% accuracy, 89.52% precision, 88.98% recall, and an F1-score of 89.25%.”
InfluentialRevolutionizing Cyber-Bullying Detection with the BullyNet Deep Learning Framework
Sathea Sree, S., & Nalini Joseph, L. (2025) · International Research Journal of Multidisciplinary Technovation · doi:10.54392/irjmt2524 · Cited by 6
Soft Computing Techniques for Detecting Cyberbullying in Social Multimedia Data
Jing, Y., Ma, H., Ansari, A. S., Sucharitha, G., Omarov, B. S., Kumar, S., Mohammadi, M. S., & Alyamani, K. A. Z. (2023) · Journal of Data and Information Quality · doi:10.1145/3604617 · Cited by 6
“[37] 2022 DEA-RNN Data set of 10000 tweets Random RNN based technique proposed”
MethodAzar, A. T., Noori, H. M., Mahlous, A. R., Alkhayyat, A. H., & Ibraheem, I. K. (2024) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.8314 · Cited by 6
“In [16], DEA-RNN, a hybrid DL approach, was used to locate cyberbullying by fine-tuning Elman RNN variables with the optimized Dolphin Echolocation Algorithm (DEA).”
MethodA Survey on Cybercrime Using Social Media
Abdalrdha, Z. K., Al-Bakry, A. M., & Farhan, A. K. (2023) · Iraqi Journal for Computers and Informatics · doi:10.25195/ijci.v49i1.404 · Cited by 5
“[37] Primarily concentrating on the issue of Twitter's”
BackgroundMethodInfluentialA two-stage cyberbullying detection based on multi-view features and decision fusion strategy
Li, T., Zeng, Z., & Sun, S. (2025) · Applied Intelligence · doi:10.1007/s10489-024-06049-x · Cited by 5
Wong, J. K. W., Chung, K. K., Lo, Y. W., Lai, C. Y., & Mung, S. W. Y. (2025) · Scientific Reports · doi:10.1038/s41598-024-82079-2 · Cited by 5
“Machine learning algorithms have been explored for their potential to detect cyberbullying on social media platforms 11–15 .”
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
“[23] proposed the DEA_RNN model that detects cyberbullying messages in online SNS.”
BackgroundKahate, S. A., & Raut, A. D. (2023) · 2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT) · doi:10.1109/icitiit57246.2023.10068711 · Cited by 4
“In a follow-up study, researchers [11], [12], [13], [14] using topic models and statistical metrics like (TFIDF) and (LDA) to glean information of significance along with user-activities-content triangular view (UAC TV) from texts.”
BackgroundWasid, 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
Offensive Language Detection on Online Social Networks using Hybrid Deep Learning Architecture
Kazbekova, G., Ismagulova, Z., Kemelbekova, Z., Tileubay, S., Baimurzayev, B., & Bazarbayeva, A. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0141180 · Cited by 4
“Studies such as those by Rathakrishnan, A., & Sathiyanarayanan [19] and Murshed et al. [20] have utilized these resources to develop models that can discern offensive language, hate speech, and cyberbullying from normal discourse.”
MethodDoes Part of Speech Have an Influence on Cyberbullying Detection?
Huang, J., Ding, R., Zheng, Y., Wu, X., Chen, S., & Jin, X. (2023) · Analytics · doi:10.3390/analytics3010001 · Cited by 4
Saqia, B., Khan, K., Rahman, A. U., Khan, A., & Jain, D. K. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-20757-7 · Cited by 4
RetractedEnhancing 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
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
“Murshed et al. [13] examine the hybrid DL technique named deep autoencoder with recurrent neural network (DEA-RNN), for detecting CB on Twitter social media networks.”
KNCD: Knowledge-Enhanced Network for Cyberbullying Detection
Yan, Y., Du, W., Sun, Y., & Yin, D. (2023) · International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI) · doi:10.1109/ichci58871.2023.10277885 · Cited by 4
Detecting cyberbullying in social media using text analysis and ensemble techniques
Kumar, Y. J. N., Vanapatla, R. R., Pinamoni, V. K., Kandukuri, J., Almusawi, M., Aravinda, K., Kansal, L., & Kalra, R. (2024) · E3S Web of Conferences · doi:10.1051/e3sconf/202450701069 · Cited by 4
Abikoye, O. C., Gboyega, O., Ogundokun, R. O., Babatunde, A. O., & Lee, C. (2024) · Security and Privacy · doi:10.1002/spy2.480 · Cited by 3
“Model Accuracy [35] Fuzzy support vector machine 47% [16] Decision tree with C4.5 78.5% [15] SVM — [27] DEA-RNN 90.45% Proposed study Maximum entropy model 77% confusion matrix, contributing to the comprehensive evaluation of the solution.”
InfluentialCan 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
“Murshed et al. (2022) recently proposed a hybrid technique for cyberbullying detection on Twitter.”
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
“Murshed et al. [7] have presented a method of composing word embedding using RNN.”
MethodSentiment Analysis for Product Reviews using Long Short-Term Memory with Layer Normalization
Raghunathan, N., Shiwakoti, P., Monika, P., Anbarasi, M. S., & Shanmugam, G. S. (2023) · International Conference on the Advancements of Artificial Intelligence in African Context (AAIAC) · doi:10.1109/aaiac60008.2023.10465282 · Cited by 3
“Another paper [19] introduces a DEA-RNN hybrid deep learning model for detecting cyberbullying (CB) on Twitter which combines Elman-type RNN with an optimized Dolphin Echolocation Algorithm (DEA).”
MethodLakkshmanan, A., Kandadi, R. R., & Manda, S. A. (2024) · International Conference on Communication and Electronics Systems (ICCES) · doi:10.1109/icces63552.2024.10860128 · Cited by 3
Alsuwaylimi, A. A., & Alenezi, Z. S. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.061674 · Cited by 3
“In [1], authors used a hybrid deep learning approach called DEA-RNN for cyberbullying detection on Twitter and achieved 86.61% accuracy, 85.94% precision, 84.14% recall, and 85.54% F1-score.”
InfluentialTowards Enhanced Cyberbullying Detection: A Unified Framework with Transfer and Federated Learning
Kumari, C., & Kaur, M. (2025) · Systems · doi:10.3390/systems13090818 · Cited by 2
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
“In [20] Random Forests, Recurrent Neural Network (RNN), SVM, POS tags, function words, and content word features Bidirectional Long Short-Term Memory (Bi-LSTM), and Multinomial Naive Bayes (MNB) (RF) were used to achieve accuracy of 90.45% based on data from Twitter.”
MethodInfluentialSreelatha, G. (2023) · International Journal of Advances in Business and Management Research · doi:10.60072/ijabmr.2023.v1i01.002 · Cited by 2
A Study on the Detection of Cyberbullying using CNN with IbI Logics Algorithm (ILA)
Karthik, K., Hema, R., Jayashree, S., & Lakshedha, P. (2024) · International Conference on Mobile Computing and Sustainable Informatics (ICMCSI) · doi:10.1109/icmcsi61536.2024.00019 · Cited by 2
“The research's [14]findings demonstrated that, while using the Twitter dataset and the combined Facebook and Twitter dataset, the ensemble's machine learning technique detected cyberbullying with an accuracy of 97.3 percent and 90.97%, respectively[15].”
BackgroundInfluentialMahaveerakannan, R., Anitha, C., & S, M. D. M. (2024) · World Conference on Communication & Computing (WCONF) · doi:10.1109/wconf61366.2024.10692193 · Cited by 2
“A number of automatic hate speech detection methods have been developed thanks to recent advances in Natural Language Processing (NLP), [11].”
BackgroundUrdu Toxic Comment Classification With PURUTT Corpus Development
Saeed, H. H., Khalil, T., & Kamiran, F. (2025) · IEEE Access · doi:10.1109/access.2025.3535862 · Cited by 4
“Murshed et al. proposed a combination of RNN and optimized Dolphin Echolocation Algorithm, the DEA-RNN [38] and achieved 90.45% accuracy on a 130000 English tweets dataset collected through Twitter API.”
InfluentialSPFHN: SPINAL PYRAMID FORWARD HARMONIC NETWORK FOR MENTAL HEALTH PREDICTION
Kshirsagar, V. G., Yadav, S. K., & Karande, N. D. (2025) · Biomedical Engineering Applications Basis and Communications · doi:10.4015/s1016237225500048 · Cited by 2
Applied intelligence with deep learning assisted automated sarcasm recognition in twitter data
Alzaidi, M. S. A., Alsudais, T. A., Eltahir, M. M. E., Ebad, S. A., Alshuhail, A., Alshahrani, M., & Alanazi, S. (2025) · Alexandria Engineering Journal · doi:10.1016/j.aej.2025.05.041 · Cited by 2
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
Tariq, Z., Abid, M., Mustafa, A., & Mahmoud, M. (2025) · SPE Journal · doi:10.2118/222801-pa · Cited by 2
Khalid, M., Mushtaq, M. F., Akram, U., Safran, M., Alfarhood, S., & Ashraf, I. (2025) · Scientific Reports · doi:10.1038/s41598-025-10661-3 · Cited by 4
Selvakumar, M., & Kathiravan, A. V. (2023) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v11i8s.7191 · Cited by 2
Gunasekaran, S., Abhishta, S., Merin, E. J. G., Karthikeyan, N., & Neethu Das, H. (2025) · International Conference on Cognitive Robotics and Intelligent Systems (ICC-ROBINS) · doi:10.1109/icc-robins64345.2025.11086313 · Cited by 1
“Their flexibility to new and changing language patterns was limited by manually created parameters such word frequency and sentiment scores [4].”
Benny, N., Tijo, N., Varghese, S. A., Manoj, V. L., & Thomas, J. (2025) · International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS) · doi:10.1109/access65134.2025.11135899 · Cited by 2
“DEA-RNN, a model introduced by Murshed et al. [4] is designed specifically for the Twitter environment that uses deep learning in conjunction with emotion analysis.”
Chakarverti, M., Goswami, A., & Kaliyar, R. K. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.115170 · Cited by 2
Yang, X., Lv, W., Reusens, M., Zhang, B., & Baesens, B. (2026) · Information Processing & Management · doi:10.1016/j.ipm.2026.104903 · Cited by 1
Bullying Tweets Detection using CNN- Attention
Alhlou, A., & Alam, A. (2023) · International Journal on Cybernetics & Informatics · doi:10.5121/ijci.2023.120106 · Cited by 9
Detection and Classification of Cyberbullying Using CR*
Gayathri, R., Lydia Beryl, D., Gowtham, M., Naveen Kumar, N., & Anbarasi, M. S. (2023) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2023.49984 · Cited by 1
Detecting Virtual Harassment in Social Media Using Machine Learning
Benassou, L. F., Bendaouia, S., Salem, O., & Mehaoua, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-59933-0_13 · Cited by 1
A Comparison Study of Cyberbullying Detection Using Various Machine Learning Algorithms
Jalda, C. S., Polimetla, U. B., Nanda, A. K., & Nanda, S. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-61298-5_4 · Cited by 1
Revealing Online Threats Through Leveraging RNN Models for Advanced Cyberbullying Detection
Rachidi, R., Errami, M., Ouassil, M. A., Jebbari, M., Cherradi, B., & Silkan, H. (2024) · International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) · doi:10.1109/iraset60544.2024.10548957 · Cited by 1
“Authors in [28] proposed a hybrid deep learning system for detecting bullying in tweets.”
BackgroundCyber-Flashing Identification Method of Analysing Human Skin on Social Media Platforms
Karpagam, M., Chen, T., & Sathiyanarayanan, M. (2024) · International Conference on Science Technology Engineering and Mathematics (ICONSTEM) · doi:10.1109/iconstem60960.2024.10568789 · Cited by 1
Al-shathry, N. I., Alghamdi, M. H., AL-DOBAIAN, A. S., Darem, A. A., Alotaibi, S. D., Almanea, M. M., Alghamdi, B. M., & Sorour, S. E. (2024) · Fractals · doi:10.1142/s0218348x25400523 · Cited by 1
Predictive Modeling of Clay-Bound Water in Carbonate Reservoirs Using Machine Learning Techniques
Tariq, Z., Abid, M., Mustafa, A., Alkhowaildi, M., & Mahmoud, M. (2024) · Abu Dhabi International Petroleum Exhibition & Conference (ADIPEC) · doi:10.2118/222801-ms · Cited by 1
Jha, A., Jaiswal, A., Singh, A., Swain, S., & Aggarwal, E. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-6726-7_19 · Cited by 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
“…proposed by Ibrahim et al. [7] is based on uncertainty in the prediction by neural network to act up upon what was described by Murshed et al. [8], as developers have applied the hybrid deep learning model for countering cyberbullying on the platform of Twitter, which reconfirms effectiveness…”
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
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
“Moreover, these kernel sizes are much bigger than those used in order to run the parallel convolution layer cascade [11] - [13].”
A Monitoring System for Cyber Bullying Activities using Stochastic Gradient Descent Classifier
Budhe, P., & Rane, D. (2023) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2023.54329
Assessing the Efficiency of Machine Learning Algorithms in Detecting Cyberbullying on Twitter
Journal, I. (2023) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem26289
Cyber Analyzer—A Machine Learning Approach for the Detection of Cyberbullying—A Survey
Shweta, S., Mundada, M. R., Sowmya, B. J., & Meeradevi, M. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-6586-1_40
An Deep Convolutional Neural Networks are used to Detect Cyberbullying on Social Networks.
Rawat, B. S. (2023) · IEEE Global Conference for Advancement in Technology (GCAT) · doi:10.1109/gcat59970.2023.10353375
“There is also discussion of the justifications for constructing prediction models to limit violent behavior in social media [6].”
BackgroundAluvala, S., Balamurugan, V., Jeyasudha, J., Kumar, R., & Pradeep Kumar, S. (2023) · International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT) · doi:10.1109/easct59475.2023.10392444
Cyberbullying Detection in Twitter Using Deep Learning Model Techniques
Seetharaman, A. R., & Jahankhani, H. (2024) · Advanced sciences and technologies for security applications · doi:10.1007/978-3-031-47594-8_7
Regac: Multi-Class Offensive Content Identification Using Graphical ApproachPreprint
Chinivar, S., Roopa, M. S., Arunalatha, J. S., & Venugopal, K. R. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4772816
Conversation Graph Construction Approach of Cyberbully Detection Using Bully Scores
Valliyammai, C., Manikandan, D., Kumar, G. S. N., Keerthika, M., & Kavin, B. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-99-9486-1_35
Detecting Cyberbullying attacks in the Social media platform using the SVM method
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Examining Machine Learning Approaches for Detecting Cyberbullying in Social Media Content
(2024) · Journal of System and Management Sciences · doi:10.33168/jsms.2024.0817
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
“A different text, referred to as [4], addresses the rising incidence of online harassment on social networking platforms, especially among youth.”
BackgroundJaved, N., Ahmed, T., & Faisal, M. (2024) · International Journal of Bullying Prevention · doi:10.1007/s42380-024-00282-1 · Cited by 1
“Cleaning text is a pivotal preprocessing stage in natural language processing (NLP) endeavors, encompassing the handling of cyber-bullying text (Aldhyani et al., 2022; Muneer & Fati, 2020; Murshed et al., 2022).”
InfluentialVictimizing Cyberbullying Mental Illness Through Social Media
Choesang, T., & Gokulapriya, R. (2025) · Smart innovation, systems and technologies · doi:10.1007/978-981-97-7717-4_20
A survey on cyber bulling predictive analysis on Twitter data with multi-model supervised technique
Sakthivel, K., Palani, N., Shanmugam, R., & Govindarajan, S. (2025) · AIP conference proceedings · doi:10.1063/5.0260530
A GRU-Driven Machine Learning Model for Detecting Cyberbullying on Twitter
Saraswathi, P., N., J., Prabha, M., & Saranya, R. (2024) · International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) · doi:10.1109/icses63760.2024.10910913
“This model focuses on using the Elman type of RNN based Recurrent Neural Networks to optimize the performance of the RNN parameters using the newly proposed Dolphin Echolocation Algorithm (DEA) [17].”
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
“Paper [11] discusses a hybrid deep learning approach, DEA-RNN, for cyberbullying detection on Twitter.”
Metadata
- Authors
- Murshed, B. A. H., Abawajy, J., Mallappa, S., Saif, M. A. N., & Al-Ariki, H. D. E.
- Affiliations
- University of Mysore (IN)
- Deakin University (AU)
- Visvesvaraya Technological University (IN)
- Taiz University (YE)
- Venue
- IEEE Access
- Published
- 2022
- Volume
- 10
- Pages
- 25857-25871
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4213430631
- Cited by
- 137
- References
- 60
- Citations by year
- 2026: 23 · 2025: 39 · 2024: 35 · 2023: 34 · 2022: 6
- FWCI
- 9.38
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Deakin Research Online (Deakin University) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Greater South Information System
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Keywords
- cyberbullying detection, Twitter, Elman neural network, metaheuristic parameter tuning, hybrid deep learning model, social media safety, tweet classification
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