Building towards Automated Cyberbullying Detection: A Comparative Analysis
Open accessAbstract
The increased use of social media among digitally anonymous users, sharing their thoughts and opinions, can facilitate participation and collaboration. However, this anonymity feature which gives users freedom of speech and allows them to conduct activities without being judged by others can also encourage cyberbullying and hate speech. Predators can hide their identity and reach a wide range of audience anytime and anywhere. According to the detrimental effect of cyberbullying, there is a growing need for cyberbullying detection approaches. In this survey paper, a comparative analysis of the automated cyberbullying techniques from different perspectives is discussed including data annotation, data preprocessing, and feature engineering. In addition, the importance of emojis in expressing emotions as well as their influence on sentiment classification and text comprehension leads us to discuss the role of incorporating emojis in the process of cyberbullying detection and their influence on the detection performance. Furthermore, the different domains for using self-supervised learning (SSL) as an annotation technique for cyberbullying detection are explored.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
A survey paper delves into the necessity for enhanced and more precise detection techniques, along with the exploration of alternative annotation methods.
“A survey paper delves into the necessity for enhanced and more precise detection techniques, along with the exploration of alternative annotation methods [153].”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [153]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Convolutional Neural Networks for Sentence Classification
Kim, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1181 · Cited by 14,831 · see citing works
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., & Soricut, R. (2019) · arXiv · doi:10.48550/arxiv.1909.11942 · Cited by 4,308 · 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
Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey
Jing, L., & Tian, Y. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2992393 · Cited by 2,036 · 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
Self-supervised Learning: Generative or Contrastive
Liu, X., Zhang, F., Hou, Z., Mian, L., Wang, Z., Zhang, J., & Tang, J. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3090866 · Cited by 1,890 · see citing works
"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection
Wang, W. Y. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-2067 · Cited by 1,822 · see citing works
Felbo, B., Mislove, A., Søgaard, A., Rahwan, I., & Lehmann, S. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1169 · Cited by 815 · see citing works
Predicting the Type and Target of Offensive Posts in Social Media
Zampieri, M., Malmasi, S., Nakov, P., Rosenthal, S., Farra, N., & Kumar, R. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1144 · Cited by 781 · see citing works
Felbo, B., Mislove, A. E., Søgaard, A., Rahwan, I., & Lehmann, S. (2017) · Research at the University of Copenhagen (University of Copenhagen) · Cited by 712 · see citing works
Revisiting Self-Supervised Visual Representation Learning
Kolesnikov, A., Zhai, X., & Beyer, L. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00202 · Cited by 675 · see citing works
A C-LSTM Neural Network for Text ClassificationPreprint
Zhou, C., Sun, C., Liu, Z., & Lau, F. C. M. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.08630 · Cited by 666 · see citing works
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 · see citing works
The Referential Structure of the Affective Lexicon
Ortony, A., Clore, G. L., & Foss, M. A. (1987) · Cognitive Science · doi:10.1207/s15516709cog1103_4 · Cited by 506 · 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
An ensemble machine learning approach through effective feature extraction to classify fake news
Hakak, S., Alazab, M., Khan, S. A., Gadekallu, T. R., Maddikunta, P. K. R., & Khan, W. Z. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.11.022 · Cited by 438 · see citing works
A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media
Mozafari, M., Farahbakhsh, R., & Crespi, N. (2020) · Studies in Computational Intelligence · doi:10.1007/978-3-030-36687-2_77 · Cited by 396 · 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
Multi-Modal Sarcasm Detection in Twitter with Hierarchical Fusion Model
Cai, Y., Cai, H., & Wan, X. (2019) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.18653/v1/p19-1239 · Cited by 377 · see citing works
Rajadesingan, A., Zafarani, R., & Liu, H. (2015) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/2684822.2685316 · Cited by 365 · see citing works
Contextualized Sarcasm Detection on Twitter
Bamman, D., & Smith, N. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v9i1.14655 · Cited by 352 · see citing works
Multi-task Self-Supervised Learning for Human Activity Detection
Saeed, A., Ozcelebi, T., & Lukkien, J. (2019) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3328932 · Cited by 346 · see citing works
Learning from Bullying Traces in Social Media
Xu, J., Jun, K.-S., Zhu, X., & Bellmore, A. D. (2012) · Cited by 336 · see citing works
A transformer-based approach to irony and sarcasm detection
Potamias, R. A., Siolas, G., & Stafylopatis, A. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05102-3 · Cited by 312 · see citing works
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 · see citing works
Fracking Sarcasm using Neural Network
Ghosh, A., & Veale, T. (2016) · Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA) · doi:10.18653/v1/w16-0425 · Cited by 295 · see citing works
Modelling Context with User Embeddings for Sarcasm Detection in Social Media
Amir, S., Wallace, B., Lyu, H., Carvalho, P., & Silva, M. J. (2016) · Conference on Computational Natural Language Learning · doi:10.18653/v1/k16-1017 · Cited by 280 · see citing works
Approaches to automated detection of cyberbullying: A survey
Salawu, S., He, Y., & Lumsden, J. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2761757 · Cited by 258 · see citing works
Analyzing Labeled Cyberbullying Incidents on the Instagram Social Network
Hosseinmardi, H., Mattson, S. A., Rafiq, R. I., Han, R. Y., Lv, Q., & Mishra, S. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-27433-1_4 · Cited by 257 · see citing works
How to work a crowd: Developing crowd capital through crowdsourcing
Prpić, J., Shukla, P., Kietzmann, J., & McCarthy, I. P. (2014) · Business Horizons · doi:10.1016/j.bushor.2014.09.005 · Cited by 254 · see citing works
Cyberbullying on social networking sites: A literature review and future research directions
Chan, T. K. H., Cheung, C. M. K., & Lee, Z. W. Y. (2020) · Information & Management · doi:10.1016/j.im.2020.103411 · Cited by 219 · see citing works
Cyberbullying detection solutions based on deep learning architectures
Iwendi, C., Srivastava, G., Khan, S., & Maddikunta, P. K. R. (2023) · Multimedia Systems · doi:10.1007/s00530-020-00701-5 · Cited by 205 · see citing works
Self-supervised Learning on Graphs: Deep Insights and New DirectionPreprint
Jin, W., Derr, T., Liu, H., Wang, Y., Wang, S., Liu, Z., & Tang, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.10141 · Cited by 205 · see citing works
Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction
Bai, W., Chen, C., Tarroni, G., Duan, J., Guitton, F., Petersen, S. E., Guo, Y., Matthews, P. M., & Rueckert, D. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32245-8_60 · Cited by 189 · see citing works
Sarcasm Detection on Czech and English Twitter
Ptáċek, T., Habernal, I., & Hong, J. S. (2014) · UWE Research Repository (UWE Bristol) · Cited by 172 · see citing works
Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM
Chen, Y., Yuan, J., You, Q., & Luo, J. (2018) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3240508.3240533 · Cited by 156 · see citing works
Tweet Sarcasm Detection Using Deep Neural Network
Zhang, M., Zhang, Y., & Fu, G. (2016) · International Conference on Computational Linguistics · Cited by 153 · see citing works
Self-Supervised Domain Adaptation for Computer Vision Tasks
Xu, J., Xiao, L., & López, A. M. (2019) · IEEE Access · doi:10.1109/access.2019.2949697 · Cited by 150 · see citing works
Humans Require Context to Infer Ironic Intent (so Computers Probably do, too)
Wallace, B., Choe, D. K., Kertz, L., & Eugene Charniak, D. (2014) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/v1/p14-2084 · Cited by 143 · see citing works
Cyberbullying 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 133 · see citing works
Content-driven detection of cyberbullying on the instagram social network
Zhong, H., Li, H., Squicciarini, A., Rajtmajer, S. M., Griffin, C. H., Miller, D. J., & Caragea, C. (2016) · International Joint Conference on Artificial Intelligence · Cited by 133 · see citing works
Irony detection via sentiment-based transfer learning
Zhang, S., Zhang, X., Chan, J., & Rosso, P. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2019.04.006 · Cited by 131 · 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
Kontostathis, A., Reynolds, K., Garron, A., & Edwards, L. (2013) · ACM Web Science Conference (WebSci) · doi:10.1145/2464464.2464499 · Cited by 119 · see citing works
Unsupervised cyber bullying detection in social networks
Capua, M. D., Nardo, E. D., & Petrosino, A. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7899672 · Cited by 117 · see citing works
Mishra, A., Dey, K., & Bhattacharyya, P. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1035 · Cited by 115 · see citing works
Dadvar, M., & de Jong, F. M. G. (2012) · International World Wide Web Conference (WWW) · doi:10.1145/2187980.2187995 · Cited by 107 · see citing works
Self-Supervised Learning of Visual Features through Embedding Images into Text Topic Spaces
Gómez, L., Patel, Y., Rusiñol, M., Karatzas, D., & Jawahar, C. V. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.218 · Cited by 106 · see citing works
Crowdsourcing and Crowdfunding in the Manufacturing and Services Sectors
Allon, G., & Babich, V. (2020) · Manufacturing & Service Operations Management · doi:10.1287/msom.2019.0825 · Cited by 98 · 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
Wallace, B., Choe, D. K., & Charniak, E. (2015) · doi:10.3115/v1/p15-1100 · Cited by 93 · see citing works
Careful what you share in six seconds
Rafiq, R. I., Hosseinmardi, H., Han, R. Y., Lv, Q., Mishra, S., & Mattson, S. A. (2015) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/2808797.2809381 · Cited by 92 · see citing works
When the Timeline Meets the Pipeline: A Survey on Automated Cyberbullying Detection
Elsafoury, F., Katsigiannis, S., Pervez, Z., & Ramzan, N. (2021) · IEEE Access · doi:10.1109/access.2021.3098979 · Cited by 80 · see citing works
Cyberbullying detection on social multimedia using soft computing techniques: a meta-analysis
Kumar, A., & Sachdeva, N. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-7234-z · Cited by 73 · see citing works
Author Profiling for Abuse Detection
Mishra, P., Tredici, M. D., Yannakoudakis, H., & Shutova, E. (2018) · UvA-DARE (University of Amsterdam) · Cited by 70 · see citing works
A semantics-based measure of emoji similarity
Wijeratne, S., Balasuriya, L., Sheth, A., & Doran, D. (2017) · IEEE/WIC/ACM International Conference on Web Intelligence · doi:10.1145/3106426.3106490 · Cited by 68 · see citing works
Incorporating Emoji Descriptions Improves Tweet Classification
Singh, A., Blanco, E., & Jin, W. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1214 · Cited by 66 · see citing works
Emmery, C., Verhoeven, B., Pauw, G. D., Jacobs, G., Hee, C. V., Lefever, E., Desmet, B., Hoste, V., & Daelemans, W. M. P. (2020) · Language Resources and Evaluation · doi:10.1007/s10579-020-09509-1 · Cited by 66 · see citing works
Transformer based contextualization of pre-trained word embeddings for irony detection in Twitter
González, J. Á., Hurtado, L.-F., & Plà, F. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102262 · Cited by 66 · see citing works
Khairy, M., Mahmoud, T. M., & El‐Hafeez, T. A. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.05.080 · Cited by 66 · see citing works
A “Deeper” Look at Detecting Cyberbullying in Social Networks
Rosa, H., de Matos, D. M., Ribeiro, R., Coheur, L., & Carvalho, J. P. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489211 · Cited by 63 · see citing works
Applying Basic Features from Sentiment Analysis for Automatic Irony Detection
Hernández-Farías, I., Benedí, J.-M., & Rosso, P. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19390-8_38 · Cited by 62 · see citing works
Cyberbullying Detection using Time Series Modeling
Potha, N., & Μaragoudakis, M. (2014) · doi:10.1109/icdmw.2014.170 · Cited by 61 · see citing works
Monday mornings are my fave :) #not Exploring the Automatic Recognition of Irony in English tweets
Hee, C. V., Lefever, E., & Hoste, V. (2016) · Ghent University Academic Bibliography (Ghent University) · Cited by 50 · see citing works
Detecting Nastiness in Social Media
Samghabadi, N. S., Maharjan, S., Sprague, A., Diaz-Sprague, R., & Solorio, T. (2017) · Workshop on Abusive Language Online (ALW) · doi:10.18653/v1/w17-3010 · Cited by 49 · see citing works
Pavlopoulos, J., Thain, N., Dixon, L., & Androutsopoulos, I. (2019) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s19-2102 · Cited by 49 · see citing works
Interpretable Emoji Prediction via Label-Wise Attention LSTMs
Barbieri, F., Espinosa-Anke, L., Camacho-Collados, J., Schockaert, S., & Saggion, H. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1508 · Cited by 48 · see citing works
Exploiting Emojis for Sarcasm Detection
Subramanian, J., Sridharan, V., Shu, K., & Liu, H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-21741-9_8 · Cited by 47 · see citing works
A Survey on Self-supervised Pre-training for Sequential Transfer Learning in Neural NetworksPreprint
Mao, H. H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.00800 · Cited by 45 · see citing works
Overview of the Task on Irony Detection in Spanish Variants.
Bueno, R. O., Pardo, F. M. R., Farías, D. I. H., Rosso, P., Montes-y-Gómez, M., & Medina-Pagola, J. E. (2019) · IberLEF@SEPLN · Cited by 43 · see citing works
Measuring Sentiment Annotation Complexity of Text
Joshi, A., Mishra, A., Senthamilselvan, N., & Bhattacharyya, P. (2014) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/v1/p14-2007 · Cited by 40 · see citing works
Predicting Readers’ Sarcasm Understandability by Modeling Gaze Behavior
Mishra, A., & Bhattacharyya, P. (2018) · Cognitive intelligence and robotics · doi:10.1007/978-981-13-1516-9_5 · Cited by 40 · see citing works
Unsupervised Cyberbullying Detection via Time-Informed Gaussian Mixture Model
Cheng, L., Shu, K., Wu, S., Silva, Y. N., Hall, D. L., & Liu, H. (2020) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3340531.3411934 · Cited by 40 · see citing works
Automated cyberbullying detection using clustering appearance patterns
Romsaiyud, W., Nakornphanom, K. N., Prasertsilp, P., Nurarak, P., & Konglerd, P. (2017) · International Conference on Knowledge and Smart Technology · doi:10.1109/kst.2017.7886127 · Cited by 39 · see citing works
Sentiment Analysis of Tweets Including Emoji Data
LeCompte, T., & Chen, J. (2017) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci.2017.137 · Cited by 36 · see citing works
Emoji Powered Capsule Network to Detect Type and Target of Offensive Posts in Social Media
Hettiarachchi, H., & Ranasinghe, T. (2019) · doi:10.26615/978-954-452-056-4_056 · Cited by 36 · see citing works
Combining textual features to detect cyberbullying in social media posts
Fortunatus, M., Anthony, P., & Charters, S. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.08.063 · Cited by 33 · see citing works
Linking textual and contextual features for intelligent cyberbullying detection in social media
Rezvani, N., Beheshti, A., & Tabebordbar, A. (2020) · International Conference on Advances in Mobile Computing and Multimedia (MoMM) · doi:10.1145/3428690.3429171 · Cited by 33 · see citing works
Exploring the fine-grained analysis and automatic detection of irony on Twitter
Hee, C. V., Lefever, E., & Hoste, V. (2018) · Language Resources and Evaluation · doi:10.1007/s10579-018-9414-2 · Cited by 30 · see citing works
A Hybrid Deep Learning System of CNN and LRCN to Detect Cyberbullying from SNS Comments
Bu, S.-J., & Cho, S.-B. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-92639-1_47 · Cited by 30 · see citing works
Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction
Bai, W., Chen, C., Tarroni, G., et al. (2019) · City Research Online (City University London) · doi:10.48550/arxiv.1907.02757 · Cited by 30 · see citing works
Exploring the Realization of Irony in Twitter Data
Hee, C. V., Lefever, E., & Hoste, V. (2016) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2u588fkk7io3 · Cited by 29 · see citing works
Deep Learning Cyberbullying Detection Using Stacked Embbedings Approach
Mahlangu, T., & Tu, C. (2019) · International Conference on Soft Computing & Machine Intelligence (ISCMI) · doi:10.1109/iscmi47871.2019.9004292 · Cited by 28 · see citing works
Self-supervised Motion Learning from Static Images
Huang, Z., Zhang, S., Jiang, J., Tang, M., Jin, R., & Ang, M. H. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00133 · Cited by 28 · see citing works
Analysis of cyberbullying tweets in trending world events
Cortis, K., & Handschuh, S. (2015) · International Conference on Knowledge Management and Knowledge Technologies · doi:10.1145/2809563.2809605 · Cited by 24 · see citing works
Make It Possible: Multilingual Sentiment Analysis without Much Prior Knowledge
Lin, Z., Jin, X., Xu, X., Wang, Y., Tan, S., & Cheng, X. (2014) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat.2014.83 · Cited by 22 · see citing works
A deep-learning framework to detect sarcasm targets
Patro, J., Bansal, S., & Mukherjee, A. (2019) · doi:10.18653/v1/d19-1663 · Cited by 21 · see citing works
“How Bullying is this Message?”: A Psychometric Thermometer for Bullying
Nand, P., Perera, R., & Kasture, A. S. (2016) · International Conference on Computational Linguistics · Cited by 18 · see citing works
Self-supervised learning of visual features through embedding images into text topic spacesPreprint
Gómez, L., Patel, Y., Rusiñol, M., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.08631 · Cited by 18 · see citing works
Attending the Emotions to Detect Online Abusive Language
Samghabadi, N. S., Hatami, A., Shafaei, M., Kar, S., & Solorio, T. (2020) · Workshop on Online Abuse and Harms (WOAH) · doi:10.18653/v1/2020.alw-1.10 · Cited by 18 · see citing works
Self-Supervised Learning Based Anomaly Detection in Online Social Media
CHRIST, Kokatnoor, S. A., Balachandran, K., & CHRIST (2020) · International journal of intelligent engineering and systems · doi:10.22266/ijies2020.0630.40 · Cited by 15 · see citing works
Sarcastic Sentiment Detection Based on Types of Sarcasm Occurring in Twitter Data
Bharti, S. K., Pradhan, R., Babu, K. S., & Jena, S. K. (2017) · International Journal on Semantic Web and Information Systems · doi:10.4018/ijswis.2017100105 · Cited by 12 · see citing works
Sarcasm Target Identification: Dataset and An Introductory Approach
Joshi, A., Goel, P., Bhattacharyya, P., & Carman, M. (2018) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2xck5kccxygt · Cited by 11 · see citing works
Farag, N., El-Seoud, S. A., McKee, G., & Hassan, G. (2019) · International Conference on Software and Information Engineering (ICSIE) · doi:10.1145/3328833.3328869 · Cited by 10 · see citing works
Emoticon and Text Sarcasm Detection in Sentiment Analysis
Gupta, S., Singh, R., & Singla, V. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-0029-9_1 · Cited by 10 · see citing works
Cyberbully Image and Text Detection using Convolutional Neural Networks
Drishya, S. V., Saranya, S., Sheeba, J. I., & Devaneyan, S. P. (2019) · CiiT international journal of fuzzy systems · Cited by 7 · see citing works
Measuring the effects of emojis on Turkish context in sentiment analysis
Yurtöz, Ç. Ü., & Parlak, İ. B. (2019) · International Symposium on Digital Forensics and Security (ISDFS) · doi:10.1109/isdfs.2019.8757554 · Cited by 7 · see citing works
A Detailed Survey On Cyberbullying in Social Networks
Krithika, V., & Priya, V. (2020) · International Conference on Emerging Trends in Information Technology and Engineering (ICETITE) · doi:10.1109/ic-etite47903.2020.031 · Cited by 6 · see citing works
Exploiting Deep Neural Networks for Tweet-based Emoji Prediction
Coman, A. C., Zara, G., Nechaev, Y., Barlacchi, G., & Moschitti, A. (2018) · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 7
- [19]Farias D. Patti V. Rosso P. Irony Detection in Twitter: The Role of Affective Content 2016 New York, NY, USA ACM Transactions on Internet Technology
- [23]Cheng L. Li J. Silva Y. N. Hall D. L. Liu H. XBully: cyberbullying detection within a multi-modal context Proceedings of the twelfth acm international conference on web search and data mining February 2019 Melbourne, Australia
- [35]Paul S. Saha S. CyberBERT: BERT for Cyberbullying Identifcation 2020 Heidelberg, Germany Springer
- [37]Tripathy J. K. Chakkaravarthy S. S. Satapathy S. C. Sahoo M. Vaidehi V. ALBERT-based Fne-Tuning Model for Cyberbullying Analysis 2020 Heidelberg, Germany Springer
- [81]Sanchez H. Kumar S. Twitter bullying detection Ser. NSDI 2011 12
- [86]Engman L. Automatic detection of cyberbullying on social media 2016 Sweden, Australia Umea University Master’s Thesis
- [106]Salawu S. He Y. Lumsden J. Approaches to Automated Detection of Cyberbullying: A Survey 2017 TN, USA IEEE Transactions On Affective Computing
Cited by
Sorted by citations
Cyberbullying detection for low-resource languages and dialects: Review of the state of the art
Mahmud, T., Ptaszynski, M. E., Eronen, J., & Masui, F. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103454 · Cited by 172 · see citing works
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
AI Techniques and Applications for Online Social Networks and Media: Insights From BERTopic Modeling
Nedungadi, P., Veena, G., Tang, K., Menon, R. R. K., & Raghu Raman, R. (2025) · IEEE Access · doi:10.1109/access.2025.3543795 · Cited by 51 · see citing works
A Deep Analysis of Textual Features Based Cyberbullying Detection Using Machine Learning
Mahmud, M. I., Mamun, M., & Abdelgawad, A. (2022) · IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) · doi:10.1109/gcaiot57150.2022.10019058 · Cited by 41 · see citing works
Cyberbullying detection and machine learning: a systematic literature review
Balakrisnan, V., & Kaity, M. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10553-w · Cited by 23 · see citing works
Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms
Philipo, A. G., Sarwatt, D. S., Ding, J., Daneshmand, M., & Ning, H. (2026) · ACM Computing Surveys · doi:10.1145/3785654 · Cited by 14 · see citing works
Detection of Cyberbullying Tweets in Twitter Media Using Random Forest Classification
Thangarasu, G., & Alla, K. R. (2023) · IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE) · doi:10.1109/iscaie57739.2023.10165118 · Cited by 13 · see citing works
UA-HSD-2025: Multi-Lingual Hate Speech Detection from Tweets Using Pre-Trained Transformers
Ahmad, M., Waqas, M., Hamza, A., Usman, S., Batyrshin, I., & Sidorov, G. (2025) · Computers · doi:10.3390/computers14060239 · Cited by 11 · see citing works
Al-Harigy, L. M., Al-Nuaim, H. A., Moradpoor, N., & Tan, Z. (2024) · International Journal of Data Science and Analytics · doi:10.1007/s41060-024-00607-9 · Cited by 10 · see citing works
Self-citationFati, S. M., Mahdi, M. A., Hazber, M. A. G., Ahamad, S., Saad, S. A., Ragab, M. G., & Al-Shalabi, M. (2025) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2025.063092 · Cited by 6 · see citing works
Building Prediction Model for Detecting Cyberbullying using TikTok Comments
Prameswari, B. A., Oktaviani, H. S., Wicaksono, T. R., Leonard, B. P., Achmad, S., & Sutoyo, R. (2023) · IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE) · doi:10.1109/icraie59459.2023.10468424 · Cited by 5 · see citing works
Leveraging deep learning to combat cyberbullying on social media
Vijayarani, M., Balamurugan, G., Govindan, R., & Sevak, S. (2025) · Industrial Psychiatry Journal · doi:10.4103/ipj.ipj_47_25 · Cited by 3 · see citing works
Al-Harigy, L. M., Al-Nuaim, H. A., Moradpoor, N., & Tan, Z. (2022) · International Conference on Computational Intelligence and Communication Networks (CICN) · doi:10.1109/cicn56167.2022.10008274 · Cited by 3 · see citing works
Self-citationCyber Social Threats: A Data-centric AI Perspective
Kulkarni, A. (2025) · ACM Southeast Regional Conference (ACMSE) · doi:10.1145/3696673.3723071 · Cited by 2 · see citing works
Guo, X., Adnan, H. M., & Abidin, M. Z. Z. (2025) · Society · doi:10.1007/s12115-025-01119-3 · Cited by 2 · see citing works
A Hybrid BERT-CNN with Multihead Self-Attention for Automated Cyberbullying Detection
Yerekesheva, M., Akhmetova, O. S., Kaziyeva, A. N., Sultan, D., Toktarova, A., Abdrakhmanov, R., & Tolep, A. S. (2026) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.12741 · Cited by 2 · see citing works
Multi-modal Cyberbullying Detection with Severity Analysis Using Deep-Tensor Fusion Framework
Singh, N. M., & Sharma, S. K. (2025) · International Journal of Computer Network and Information Security · doi:10.5815/ijcnis.2025.03.08 · Cited by 2 · see citing works
Maqsood, S., & Bano, T. (2025) · Synergy International Journal of Multidisciplinary Studies · doi:10.63960/sijmds-2025-2138 · Cited by 2 · see citing works
Muthusami, R., Saritha, K., Sarwe, D. U., Megala, A., & Pravina, C. T. D. (2024) · Journal of The Institution of Engineers (India) Series B · doi:10.1007/s40031-024-01181-1 · Cited by 1 · see citing works
Đurić, A., Havzi, S., Nikolić, D., & Stefanović, D. (2025) · International Symposium INFOTEH-JAHORINA (INFOTEH) · doi:10.1109/infoteh64129.2025.10959261 · Cited by 1 · see citing works
Abusaqer, M., & Saquer, J. M. (2025) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac65507.2025.00216 · Cited by 1 · see citing works
Fikry, W. A. R. W. I., & Liew, J. S. Y. (2025) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-96-7749-8_29 · Cited by 1 · see citing works
Hernandez‐Pena, L., Koch, J., Raine, A., & Wagels, L. (2026) · Review of General Psychology · doi:10.1177/10892680251412856 · Cited by 1 · see citing works
Akwa, G. (2026) · AI and Ethics · doi:10.1007/s43681-026-01054-x · Cited by 1 · see citing works
Sarcasm Detection in Libyan Arabic Dialects Using Natural Language Processing Techniques
Alakrot, A., Dogman, A., & Ammer, F. (2024) · IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA) · doi:10.1109/mi-sta61267.2024.10599695 · Cited by 1 · see citing works
Smart Language Checker: A Machine Learning Solution for Offensive Language detection in Social Media
Kavitha, S. K., Anchitaalagammai, J. V., Murali, S. R. V., Deepalakshmi, R., Himal, L. R., & Suryakanth, M. S. (2023) · International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) · doi:10.1109/icdsaai59313.2023.10452454
Dificultades en la prevención de la violencia contra los niños
Izquierdo, P. F. (2025) · Razón y fe: Revista hispanoamericana de cultura · doi:10.14422/ryf.vol289.i1466.y2025.002
Kuri, M. M., & Pathak, G. R. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-06691-6_40
Gupta, A., Matta, P., & Pant, B. (2025) · International Journal of Information Technology · doi:10.1007/s41870-025-02872-8
Adaptive multilingual cyberbullying detection via grammatical evolution
Ismail, W. S., Ullah, H., Adnan, M., & Ullah, F. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3740
Kuri, M. M., & Pathak, G. R. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-27163-1_24
Stacking Ensemble vs IndoBERT for Indonesian Cyberbullying Detection
Justin, J., Howen, K., Wijaya, A., Jabar, B. A., & Halim, R. (2026) · International Conference on Information Management and Technology (ICIMTech) · doi:10.1109/icimtech202671078.2026.11699022
The linguistic features of isiZulu cyberbullying: A pilot study
Naidoo, S., & Ntuli, T. (2026) · Southern African Linguistics and Applied Language Studies · doi:10.2989/16073614.2026.2694600
- Authors
- Al-Harigy, L. M., Al-Nuaim, H. A., Moradpoor, N., & Tan, Z.
- Affiliations
- King Abdulaziz University (SA)
- Edinburgh Napier University (GB)
- Corresponding author
- Al-Harigy, Lulwah M.
- Venue
- Computational Intelligence and Neuroscience
- Publisher
- Wiley
- Published
- 2022-06-25
- Volume
- 2022
- Pages
- 1-20
- ISSN
- 1687-5265 (ISSN-L)
- 1687-5273
- PMC
- PMC9250443
- OpenAlex
- W4283454882
- Cited by
- 32
- References
- 99
- Citations by year
- 2026: 7 · 2025: 15 · 2024: 3 · 2023: 5 · 2022: 2
- FWCI
- 0.44
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Computational Intelligence and Neuroscience (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Edinburgh Napier Research Repository (Edinburgh Napier University) (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Edinburgh Napier Research Repository (Edinburgh Napier University) (submittedVersion)
- 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, social media, online anonymity, hate speech, data annotation, data preprocessing, feature engineering, emojis, sentiment classification, self-supervised learning
- MeSH terms
- Cyberbullying, Humans, Social Media
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