The Use of a Large Language Model for Cyberbullying Detection
Bayode Ogunleye Babitha Dharmaraj
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.