The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
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
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Cyberbullying Detection: An Ensemble Based Machine Learning Approach
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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
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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
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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