The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Cyberbullying detection solutions based on deep learning architectures
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In the citing paper
“In addition, understanding the context of the text or images increases the chance of providing better accuracy [19-23].”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [21]
In the citing paper
“Iwendi et al. [21] used Bi-LSTM, GRU, LSTM, and RNN models for cyberbullying detection over the DISCo Kaggle dataset where Bi-LSTM outperformed other models with an accuracy score of 82.18%.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [21]
In the citing paper
“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 [21]
In the citing paper
“Several deep-learning-based cyberbullying detection methods have been conducted by using individual models and presenting the performance of the models with popular evaluation metrics [21,134,149,150].”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [21]
In the citing paper
“A few studies [21,134] exploit RNN-based techniques, i.e., LSTM, BiLSTM, RNN, etc., while some other studies [5,137] use CNN-based techniques (i.e., CNN, PCNN, Char-CNNS, etc.) to detect cyberbullying from different sources.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [21]
In the citing paper
“Iwendi et al. [21] applied three DL models: Bi-LSTM, LSTM, and RNN, to investigate the performance of DL algorithms in identifying bullying (i.e., insults) in social media. They discovered that Bi-LSTM outperforms other models in terms of accuracy and F1 scores after extensive testing.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [21]
Citations
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Automatic cyberbullying detection: A systematic review
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Self-citationCyberbullying Detection Based on Semantic-Enhanced Marginalized Denoising Auto-Encoder
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Cryptocurrency malware hunting: A deep Recurrent Neural Network approach
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Self-citationSuspicious activity detection using deep learning in secure assisted living IoT environments
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Self-citationOptimized Blockchain Model for Internet of Things based Healthcare Applications
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Self-citationDetection of Cyberbullying Using Deep Neural Network
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Optimized Twitter Cyberbullying Detection based on Deep Learning
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Three Ethical Challenges of Applications of Artificial Intelligence in Cybersecurity
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Multilingual Cyberbullying Detection System
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A multi-platform dataset for detecting cyberbullying in social media
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Cyberbullying detection on social multimedia using soft computing techniques: a meta-analysis
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Cyberbullying Detection in Social Networks Using Deep Learning Based Models; A Reproducibility Study
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Cyberbullying Detection by Using Artificial Neural Network Models
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Presenting a labelled dataset for real-time detection of abusive user posts
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An IOT design for smart lighting in green buildings based on environmental factors
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Witnessing Crime through Tweets
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Murshed, B. A. H., Abawajy, J., Mallappa, S., Saif, M. A. N., & Al-Ariki, H. D. E. (2022) · IEEE Access · doi:10.1109/access.2022.3153675 · Cited by 137
Jagannathan, P., Rajkumar, S., Frnda, J., Divakarachari, P. B., & Prabu, S. (2021) · Wireless Communications and Mobile Computing · doi:10.1155/2021/5590894 · Cited by 106
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
“Iwendi et al. [21] used Bi-LSTM, GRU, LSTM, and RNN models for cyberbullying detection over the DISCo Kaggle dataset where Bi-LSTM outperformed other models with an accuracy score of 82.18%.”
BackgroundMethodInfluentialAn application to detect cyberbullying using machine learning and deep learning techniques
Raj, M., Singh, S., Solanki, K., & Selvanambi, R. (2022) · SN Computer Science · doi:10.1007/s42979-022-01308-5 · Cited by 97
Dewani, A., Memon, M. A., & Bhatti, S. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00550-7 · Cited by 91
Metadata
- Authors
- Iwendi, C., Srivastava, G., Khan, S., & Maddikunta, P. K. R.
- Affiliations
- Central South University of Forestry and Technology (CN)
- China Medical University (TW)
- Air University (PK)
- Vellore Institute of Technology University (IN)
- Corresponding author
- Srivastava, Gautam
- Venue
- Multimedia Systems
- Published
- 2020-10-13
- Volume
- 29
- Issue
- 3
- Pages
- 1839-1852
- ISSN
- 0942-4962 (ISSN-L)
- 1432-1882
- OpenAlex
- W3092739266
- Cited by
- 205
- References
- 33
- Citations by year
- 2026: 22 · 2025: 38 · 2024: 49 · 2023: 43 · 2022: 33 · 2021: 20
- FWCI
- 9.44
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Multimedia Systems (publishedVersion)
- 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, insults, social media text, bidirectional LSTM, GRU, LSTM, recurrent neural networks, text classification, online harassment
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