The DOI matches its Crossref record.
Attention Based Context Boosted Cyberbullying Detection in Social Media
Abstract
Cyberbullying detection is a rising research topic due to its paramount impact on social media users, especially youngsters and adolescents. While there has been an enormous amount of progress in utilising efficient machine learning and NLP techniques for tackling this task, recent methods have not fully addressed contextualizing the textual content to the highest possible extent. The textual content of social media posts and comments is normally long, noisy and mixed with lots of irrelevant tokens and characters, and therefore utilizing an attention-based approach that can focus on more relevant parts of the text can be quite pertinent. Moreover, social media information is normally multi-modal in nature and may contain various metadata and contextual information that can contribute to enhancing the Cyberbullying prediction system. In this research, we propose a novel machine learning method that, (i) fine tunes a variant of BERT, a deep attention-based language model, which is capable of detecting patterns in long and noisy bodies of text; (ii)~extracts contextual information from multiple sources including metadata information, images and even external knowledge sources and uses these features to complement the learner model; and (iii) efficiently combines textual and contextual features using boosting and a wide-and-deep architecture. We compare our proposed method with state-of-the-art methods and highlight how our approach significantly outperforming the quality of results compared to those methods in most cases.
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In the citing paper
“Attention-based deep learning models have been successful in identifying and categorizing cyberbullying behavior on different platforms.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [112]
In the citing paper
“These models can identify various data types, such as text, images, and videos, and can capture contextual information.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [112]
In the citing paper
“By increasing the accuracy of automated systems, attention-based deep learning models have made a significant contribution to the field of cyberbullying detection [112].”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [112]
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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
“Attention-based model [147] Twitter bullied text identification [78], social media text analysis [112], online textual harassment detection [71], contextual textual bullies [148], Instagram bullied text identification [118], Abusive Bangla Comment detection [121], Trait-based bullying detection [114]…”
BackgroundMethodCyberbullying as a Learned Behavior: Theoretical and Applied Implications
Barlett, C. P. (2023) · Children · doi:10.3390/children10020325 · Cited by 41
Deep Learning for Multi-Labeled Cyberbully Detection: Enhancing Online Safety
Islam, N., Haque, R., Pareek, P. K., Islam, M. B., Sajeeb, I. H., & Ratul, M. H. (2023) · International Conference on Data Science and Network Security (ICDSNS) · doi:10.1109/icdsns58469.2023.10245135 · Cited by 19
Singh, N. M., & Sharma, S. K. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16402-w · Cited by 20
Shah, V., Sinha, A., Navalkar, N., et al. (2023) · Journal of Smart Internet of Things · doi:10.2478/jsiot-2023-0020 · Cited by 11
Comparative Analysis of GPT Models for Detecting Cyberbullying in Social Media Platforms Threads
Islam, M. S., & Rafiq, R. I. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-63616-5_25 · Cited by 7
Human-in-the-Loop Optimization for Artificial Intelligence Algorithms
Farhood, H., Saberi, M., & Najafi, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-14135-5_7 · Cited by 3
A Study on the Detection of Cyberbullying using CNN with IbI Logics Algorithm (ILA)
Karthik, K., Hema, R., Jayashree, S., & Lakshedha, P. (2024) · International Conference on Mobile Computing and Sustainable Informatics (ICMCSI) · doi:10.1109/icmcsi61536.2024.00019 · Cited by 2
“By utilizing machine learning, it is possible to identify linguistic trends in cyberbullying posts and create an automated model for identifying cyberbullying material [1].”
MethodUrdu Toxic Comment Classification With PURUTT Corpus Development
Saeed, H. H., Khalil, T., & Kamiran, F. (2025) · IEEE Access · doi:10.1109/access.2025.3535862 · Cited by 4
“Rezvani and Beheshti combined the contextual and lingual features of 2188 Instagram posts and 7321 Twitter tweets to come up with a more robust cyberbullying detection transformer-based multi-model [44] and achieved 0.87 F1 score.”
InfluentialChow, D. V., Natania, F., Nathanael, O. T., Setiawan, K. E., & Hasani, M. F. (2023) · International Conference on Cybernetics and Intelligent System (ICORIS) · doi:10.1109/icoris60118.2023.10352294 · Cited by 1
A New Model for a Secure Social Media Application
Riad, K., & Elhoseny, M. (2022) · Applied Sciences · doi:10.3390/app12136375 · Cited by 1
A Study on Cyberbullying Detection Techniques in Online Social Networks
Kalantri, R. A., & Rajguru, S. (2023) · International Conference on Advances in Science and Technology (ICAST) · doi:10.1109/icast59062.2023.10455074 · Cited by 1
Regac: Multi-Class Offensive Content Identification Using Graphical ApproachPreprint
Chinivar, S., Roopa, M. S., Arunalatha, J. S., & Venugopal, K. R. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4772816
Metadata
- Authors
- Rezvani, N., & Beheshti, A.
- Venue
- journal of Data Intelligence
- Publisher
- Rinton Press
- Published
- 2021-11
- Volume
- 2
- Issue
- 4
- Pages
- 418-433
- ISSN
- 2577-610X (ISSN-L)
- OpenAlex
- W3203292564
- Cited by
- 13
- References
- 0
- Citations by year
- 2025: 1 · 2024: 3 · 2023: 7 · 2022: 2
- FWCI
- 1.06
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Data Intelligence (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, social media, attention mechanism, BERT fine-tuning, contextual information, multimodal features, metadata, imaging features, external knowledge sources, boosting, text classification, noisy text
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