Article
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Use of Large Language Model for Cyberbullying Detection
Version 1
: Received: 15 June 2023 / Approved: 15 June 2023 / Online: 15 June 2023 (05:35:25 CEST)
A peer-reviewed article of this Preprint also exists.
Ogunleye, B.; Dharmaraj, B. The Use of a Large Language Model for Cyberbullying Detection. Analytics 2023, 2, 694-707. Ogunleye, B.; Dharmaraj, B. The Use of a Large Language Model for Cyberbullying Detection. Analytics 2023, 2, 694-707.
Abstract
The dominance of social media has added to the channels of bullying to 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 issue and generalisation. In recent years, large language models (LLM) 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. 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 RoBERTa outperformed other models.
Keywords
BERT; Cyberbullying; RoBERTa; Language Model; Machine learning; Online abuse; Natural language processing; NLP
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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