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
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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
Cites
Tokunaga, R. S. (2010) · Computers in Human Behavior · doi:10.1016/j.chb.2009.11.014 · Cited by 2,566
Bullies Move Beyond the Schoolyard: A Preliminary Look at Cyberbullying
Patchin, J. W., & Hinduja, S. (2006) · Youth Violence and Juvenile Justice · doi:10.1177/1541204006286288 · Cited by 1,701
Detecting Offensive Language in Social Media to Protect Adolescent Online Safety
Chen, Y., Zhou, Y., Zhu, S., & Xu, H. (2012) · International Conference on Social Computing · doi:10.1109/socialcom-passat.2012.55 · Cited by 653
Ex Machina: Personal Attacks Seen at Scale
Wulczyn, E., Thain, N., & Dixon, L. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052591 · Cited by 636
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
Automatic cyberbullying detection: A systematic review
Rosa, H., Pereira, N., Ribeiro, R., Ferreira, P. C., Carvalho, J. P., Oliveira, S., Coheur, L., Paulino, P., Veiga Simão, A. M., & Trancoso, I. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2018.12.021 · Cited by 307
Chavan, V. S., & Shylaja, S. S. (2015) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2015.7275970 · Cited by 153
KeySplitWatermark: Zero Watermarking Algorithm for Software Protection Against Cyber-Attacks
Iwendi, C., Jalil, Z., Javed, A. R., Gadekallu, T. R., Kaluri, R., Srivastava, G., & Jo, O. (2020) · IEEE Access · doi:10.1109/access.2020.2988160 · Cited by 146
Self-citationCyberbullying Detection Based on Semantic-Enhanced Marginalized Denoising Auto-Encoder
Zhao, R., & Mao, K. (2016) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2016.2531682 · Cited by 138
Cryptocurrency malware hunting: A deep Recurrent Neural Network approach
Yazdinejad, A., HaddadPajouh, H., Dehghantanha, A., Parizi, R. M., Srivastava, G., & Chen, M. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106630 · Cited by 135
Self-citation
Cited by
Sorted by citations
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
