Performance analysis of transformer-based architectures and their ensembles to detect trait-based cyberbullying
Integrity signals
Registry
Cites
Statistics
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?
Transformers are deep learning models based on attention that have shown effectiveness in detecting cyberbullying.
“Transformers are deep learning models based on attention that have shown effectiveness in detecting cyberbullying [113-115].”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [114]
Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works
SQuAD: 100,000+ Questions for Machine Comprehension of Text
Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1264 · Cited by 6,208 · see citing works
Automated Hate Speech Detection and the Problem of Offensive Language
Davidson, T., Warmsley, D., Macy, M., & Weber, I. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14955 · Cited by 2,610 · see citing works
Feature Selection: A Data Perspective
Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., & Liu, H. (2018) · ACM Computing Surveys · doi:10.1145/3136625 · Cited by 2,442 · see citing works
Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter
Talat, Z., & Hovy, D. (2016) · North American Chapter of the Association for Computational Linguistics · doi:10.18653/v1/n16-2013 · Cited by 1,919 · see citing works
Deep Learning for Hate Speech Detection in Tweets
Badjatiya, P., Gupta, S., Gupta, M., & Varma, V. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3041021.3054223 · Cited by 1,253 · see citing works
Abusive Language Detection in Online User Content
Nobata, C., Tetreault, J., Thomas, A. O., Mehdad, Y., & Chang, Y. (2016) · International World Wide Web Conference (WWW) · doi:10.1145/2872427.2883062 · Cited by 1,160 · see citing works
Rasmy, L., Xiang, Y., Xie, Z., Tao, C., & Zhi, D. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00455-y · Cited by 886 · see citing works
Ambati, B. R., Deoskar, T., Johnson, M., & Steedman, M. J. (2015) · The Association for Computational Linguistics · Cited by 860 · see citing works
Hate Speech Detection with Comment Embeddings
Djuric, N., Zhou, J. L., Morris, R., Grbovic, M., Radosavljević, V., & Bhamidipati, N. L. (2015) · International World Wide Web Conference (WWW) · doi:10.1145/2740908.2742760 · Cited by 725 · see citing works
Burnap, P., & Williams, M. L. (2015) · Policy & Internet · doi:10.1002/poi3.85 · Cited by 615 · see citing works
Using Convolutional Neural Networks to Classify Hate-Speech
Gambäck, B., & Sikdar, U. K. (2017) · Workshop on Abusive Language Online (ALW) · doi:10.18653/v1/w17-3013 · Cited by 540 · see citing works
Using Machine Learning to Detect Cyberbullying
Reynolds, K., Kontostathis, A., & Edwards, L. (2011) · doi:10.1109/icmla.2011.152 · Cited by 462 · see citing works
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 · see citing works
One-step and Two-step Classification for Abusive Language Detection on Twitter
Park, J. H., & Fung, P. (2017) · Workshop on Abusive Language Online (ALW) · doi:10.18653/v1/w17-3006 · Cited by 383 · see citing works
Hate speech detection: A solved problem? The challenging case of long tail on Twitter
Zhang, Z., & Luo, L. (2018) · Semantic Web · doi:10.3233/sw-180338 · Cited by 345 · see citing works
Challenges in discriminating profanity from hate speech
Malmasi, S., & Zampieri, M. (2017) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/0952813x.2017.1409284 · Cited by 270 · see citing works
Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces
Rios, A., & Kavuluru, R. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1352 · Cited by 192 · see citing works
An approach for classification of highly imbalanced data using weighting and undersampling
Anand, A., Pugalenthi, G., Fogel, G. B., & Suganthan, P. N. (2010) · Amino Acids · doi:10.1007/s00726-010-0595-2 · Cited by 172 · see citing works
SOSNet: A Graph Convolutional Network Approach to Fine-Grained Cyberbullying Detection
Wang, J., Fu, K., & Lu, C. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9378065 · Cited by 139 · see citing works
Detecting Cyberbullying and Cyberaggression in Social Media
Chatzakou, D., Ilias Leontiadis, A., Blackburn, J., Cristofaro, E. D., Stringhini, G., Vakali, A. I., & Kourtellis, N. (2019) · ACM Transactions on the Web · doi:10.1145/3343484 · Cited by 137 · see citing works
Cheng, L., Li, J., Silva, Y. N., Hall, D. L., & Liu, H. (2019) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3289600.3291037 · Cited by 124 · see citing works
Open-world Learning and Application to Product Classification
Xu, H., Liu, B., Shu, L., & Yu, P. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313644 · Cited by 102 · see citing works
Identification and characterization of cyberbullying dynamics in an online social network
Squicciarini, A., Rajtmajer, S. M., Liu, Y., & Griffin, C. H. (2015) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/2808797.2809398 · Cited by 94 · see citing works
Sentiment Informed Cyberbullying Detection in Social Media
Dani, H., Li, J., & Liu, H. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-71249-9_4 · Cited by 92 · see citing works
Query-oriented text summarization using sentence extraction technique
Afsharizadeh, M., Ebrahimpour-Komleh, H., & Bagheri, A. (2018) · International Conference on Web Research (ICWR) · doi:10.1109/icwr.2018.8387248 · Cited by 83 · see citing works
Cyberbullying Detection in Social Networks Using Bi-GRU with Self-Attention Mechanism
Fang, Y., Yang, S., Zhao, B., & Huang, C. (2021) · Information · doi:10.3390/info12040171 · Cited by 74 · cited 3× · see citing works
“There has been few works in this domain where authors have proposed a model to detect cyberbullying without considering different traits of it from social media texts such as the work of Fang et al. (2021).”
BackgroundResultMittos, A., Zannettou, S., Blackburn, J., & Cristofaro, E. D. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7314 · Cited by 73 · see citing works
Analyzing Right-wing YouTube Channels
Ottoni, R., Cunha, E. L. T. P., Magno, G., Bernardina, P., Meira, W., & Almeida, V. A. F. (2018) · ACM Web Science Conference (WebSci) · doi:10.1145/3201064.3201081 · Cited by 70 · see citing works
Cyberbullying Detection: An Overview
Hamiza Wan Ali, W. N., Mohd, M., & Fauzi, F. (2018) · 2018 Cyber Resilience Conference (CRC) · doi:10.1109/cr.2018.8626869 · Cited by 60 · see citing works
Learning from Imbalanced Data Using Ensemble Methods and Cluster-Based Undersampling
Sobhani, P., Viktor, H. L., & Matwin, S. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-17876-9_5 · Cited by 47 · see citing works
Modeling temporal patterns of cyberbullying detection with hierarchical attention networks
Cheng, L., Guo, R., Silva, Y. N., Hall, D., & Liu, H. (2021) · ACM/IMS Transactions on Data Science · doi:10.1145/3441141 · Cited by 43 · see citing works
Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection
Nguyen, H., Zhang, C., Xia, C., & Yu, P. S. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.108 · Cited by 24 · see citing works
Am I Being Bullied on Social Media? An Ensemble Approach to Categorize Cyberbullying
Ahmed, T., Kabir, M., Ivan, S., Mahmud, H., & Hasan, K. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671594 · Cited by 20 · see citing works
Self-citationSelf-Attention for Cyberbullying Detection
Pradhan, A., Madhav, Y. V., & Bera, P. (2020) · International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA) · doi:10.1109/cybersa49311.2020.9139711 · Cited by 18 · see citing works
Does BERT Pay Attention to Cyberbullying?
Elsafoury, F., Katsigiannis, S., Wilson, S. R., & Ramzan, N. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3463029 · Cited by 18 · see citing works
Leveraging aspect-based sentiment prediction with textual features and document metadata
Korovesis, K., Alexandridis, G., Caridakis, G., Polydoras, P., & Tsantilas, P. (2020) · Hellenic Conference on Artificial Intelligence · doi:10.1145/3411408.3411433 · Cited by 4 · see citing works
Flagged first, then by citations
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 · see citing works
“…textual bullies [148], Instagram bullied text identification [118], Abusive Bangla Comment detection [121], Trait-based bullying detection [114] The method provides a simple and efficient architecture with a fixed length vector to pay attention of a sentence’s high-level meaning The model…”
Method
- Authors
- Ahmed, T., Ivan, S., Kabir, M., Mahmud, H., & Hasan, K.
- Affiliations
- Islamic University of Technology (BD)
- Corresponding author
- Ahmed, Tasnim
- Venue
- Social Network Analysis and Mining
- Published
- 2022-08-02
- Volume
- 12
- Issue
- 1
- ISSN
- 1869-5450 (ISSN-L)
- 1869-5469
- OpenAlex
- W4289523473
- Cited by
- 42
- References
- 37
- Citations by year
- 2026: 10 · 2025: 11 · 2024: 12 · 2023: 5 · 2022: 4
- FWCI
- 2.50
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Social Network Analysis and Mining (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, transformer models, ensemble learning, social media comments, Twitter, class imbalance, automated content moderation
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