Assessing Text Classification Methods for Cyberbullying Detection on Social Media Platforms

TL;DR

This study compares various text classification methods, especially large language models like BERT and GPT-2.0, for detecting cyberbullying on social media, highlighting BERT's balanced performance and efficiency.

Contribution

It provides a comprehensive evaluation of existing models for cyberbullying detection, emphasizing BERT's effectiveness and analyzing the trade-offs of different large language models.

Findings

BERT achieves 95% accuracy and F1 score in cyberbullying detection.

Generative AI models do not always outperform fine-tuned models.

Fine-tuning enhances model performance on specific datasets.

Abstract

Cyberbullying significantly contributes to mental health issues in communities by negatively impacting the psychology of victims. It is a prevalent problem on social media platforms, necessitating effective, real-time detection and monitoring systems to identify harmful messages. However, current cyberbullying detection systems face challenges related to performance, dataset quality, time efficiency, and computational costs. This research aims to conduct a comparative study by adapting and evaluating existing text classification techniques within the cyberbullying detection domain. The study specifically evaluates the effectiveness and performance of these techniques in identifying cyberbullying instances on social media platforms. It focuses on leveraging and assessing large language models, including BERT, RoBERTa, XLNet, DistilBERT, and GPT-2.0, for their suitability in this domain. The results show that BERT strikes a balance between performance, time efficiency, and computational resources: Accuracy of 95%, Precision of 95%, Recall of 95%, F1 Score of 95%, Error Rate of 5%, Inference Time of 0.053 seconds, RAM Usage of 35.28 MB, CPU/GPU Usage of 0.4%, and Energy Consumption of 0.000263 kWh. The findings demonstrate that generative AI models, while powerful, do not consistently outperform fine-tuned models on the tested benchmarks. However, state-of-the-art performance can still be achieved through strategic adaptation and fine-tuning of existing models for specific datasets and tasks.