The Use of a Large Language Model for Cyberbullying Detection
Open accessAbstract
The dominance of social media has added to the channels of bullying for perpetrators. Unfortunately, cyberbullying (CB) is the most prevalent phenomenon in today’s cyber world, and is a severe threat to the mental and physical health of citizens. This opens the need to develop a robust system to prevent bullying content from online forums, blogs, and social media platforms to manage the impact in our society. Several machine learning (ML) algorithms have been proposed for this purpose. However, their performances are not consistent due to high class imbalance and generalisation issues. In recent years, large language models (LLMs) like BERT and RoBERTa have achieved state-of-the-art (SOTA) results in several natural language processing (NLP) tasks. Unfortunately, the LLMs have not been applied extensively for CB detection. In our paper, we explored the use of these models for cyberbullying (CB) detection. We have prepared a new dataset (D2) from existing studies (Formspring and Twitter). Our experimental results for dataset D1 and D2 showed that RoBERTa outperformed other models.
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Cultural disparities and region-specific topics on social media platforms increase the diversity and quantity of user-generated instances, thereby complicating the linguistic dimensions of cyberbullying detection.
“Moreover, the presence of cultural disparities and region-specific topics on social media platforms increases the diversity and quantity of usergenerated instances, thereby complicating the linguistic dimensions of cyberbullying detection [100].”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [100]
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- Authors
- Ogunleye, B., & Dharmaraj, B.
- Affiliations
- University of Brighton (GB)
- Department of Digital, Data and Technology, Ofgem, London E14 4PU, UK
- Corresponding author
- Ogunleye, Bayode
- ORCID
- Ogunleye, Bayode
- Venue
- Analytics
- Publisher
- MDPI AG
- Published
- 2023-09-06
- Volume
- 2
- Issue
- 3
- Pages
- 694-707
- ISSN
- 2813-2203 (ISSN-L)
- OpenAlex
- W4386483953
- Cited by
- 37
- References
- 59
- Citations by year
- 2026: 11 · 2025: 14 · 2024: 9 · 2023: 3
- FWCI
- 3.48
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Analytics (publishedVersion, cc-by)
- Preprints.org (acceptedVersion, cc-by)
- University of Brighton Repository (University of Brighton) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by)
- University of Brighton Repository (University of Brighton) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- cyberbullying detection, large language models, RoBERTa, BERT, social media, class imbalance, online forums, dataset construction, Twitter
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 ›
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