# The Use of a Large Language Model for Cyberbullying Detection

arXiv: https://arxiv.org/abs/2402.04088 · HTML: https://mlsanity.com/paper/2402.04088/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 65 references that are in the map.
- First posted on arXiv 6 Feb 2024.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Ex Machina: Personal Attacks Seen at Scale](https://mlsanity.com/paper/1610.08914/) (2016)
- [Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms](https://mlsanity.com/paper/1801.06482/) (2018)
- [Detecting Cyberbullying and Cyberaggression in Social Media](https://mlsanity.com/paper/1907.08873/) (2019)
- [Comparison of Topic Modelling Approaches in the Banking Context](https://mlsanity.com/paper/2402.03176/) (2024)
- [Current Limitations in Cyberbullying Detection: on Evaluation Criteria, Reproducibility, and Data Scarcity](https://mlsanity.com/paper/1910.11922/) (2019)
- [Cyberbullying detection across social media platforms via platform-aware adversarial encoding](https://mlsanity.com/paper/2204.00334/) (2022)

## Built on by (most cited first, 4 of 4)

- [A Systematic Review of Generative AI for Teaching and Learning Practice](https://mlsanity.com/paper/2406.09520/) (2024)
- [Sentiment Informed Sentence BERT-Ensemble Algorithm for Depression Detection](https://mlsanity.com/paper/2409.13713/) (2024)
- [ID-XCB: Data-independent Debiasing for Fair and Accurate Transformer-based Cyberbullying Detection](https://mlsanity.com/paper/2402.16458/) (2024)
- [Assessing Text Classification Methods for Cyberbullying Detection on Social Media Platforms](https://mlsanity.com/paper/2412.19928/) (2024)

## Abstract (arXiv)

The dominance of social media has added to the channels of bullying for perpetrators. Unfortunately, cyberbullying (CB) is the most prevalent phenomenon in todays 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.