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
RETRACTED ARTICLE: Feature selection techniques in the context of big data: taxonomy and analysis
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MethodSelf-citationRetractedRecurrent Neural Networks for Prediction
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“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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“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
Flagged first, then by citations
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- 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
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InfluentialEnhanced Category-Feature Association Measure
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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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