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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Cites
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RETRACTED ARTICLE: Feature selection techniques in the context of big data: taxonomy and analysis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×
“There are so many Feature selection methods as mentioned in [60].”
MethodSelf-citationRetractedRecurrent Neural Networks for Prediction
Mandic, D. P., & Chambers, J. A. (2001) · Wiley eBooks · doi:10.1002/047084535x · Cited by 1,131
Detecting Hate Speech on Twitter Using a Convolution-GRU Based Deep Neural Network
Zhang, Z., Robinson, D., & Tepper, J. A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93417-4_48 · Cited by 676 · cited 1×
“Whereas the other keywords such as ban, kill, die, evil, hate, attack, terrorist, threat, racism, black, Muslim, Islam, and Islamic were suggested in [39].”
Risk factors for involvement in cyber bullying: Victims, bullies and bully–victims
Mishna, F., Khoury-Kassabri, M., Gadalla, T., & Daciuk, J. (2012) · Children and Youth Services Review · doi:10.1016/j.childyouth.2011.08.032 · Cited by 622 · cited 1×
“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
Pitsilis, G., Ramampiaro, H., & Helge Langseth, A. (2018) · Applied Intelligence · doi:10.1007/s10489-018-1242-y · Cited by 432
Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms
Agrawal, S., & Awekar, A. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-76941-7_11 · Cited by 387 · cited 1×
“The oversampling process is performed by replicating cyberbullying samples many times to balance the dataset as used in [15], [16].”
Improving Cyberbullying Detection with User Context
Dadvar, M., Trieschnigg, D., Ordelman, R., & de Jong, F. M. G. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-36973-5_62 · Cited by 384 · cited 1×
“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].”
BackgroundAl-Garadi, M. A., Varathan, K. D., & Ravana, S. D. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.05.051 · Cited by 356 · cited 1×
“[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
Research works printed but not linked: 2
- Miller, 2016, Cyberbullying and its consequences: How cyberbullying is contorting the minds of victims and bullies alike, and the law’s limited available redress, Southern California Interdiscipl. Law J., 26, 379
- 10.1109/ICICCS48265.2020.9120893 · DOI
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
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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