Paper Contents
Abstract
This paper explores the efficiency of various machine learning models for abuse detection in text, comparing traditional models (Logistic Regression, Random Forest, Decision Trees) with advanced deep learning techniques (RNNs, LSTMs, Bi-LSTMs, CNNs) and pretrained transformers (BERT, RoBERTa, DistilBERT, XLNet). The study also investigates hybrid models that combine the strengths of these individual approaches to improve accuracy. By evaluating the performance of these models on abuse detection tasks, the research aims to identify the most effective methods for automatically detecting abusive language in online content, contributing to more efficient content moderation systems.
Copyright
Copyright © 2025 Maharshi S Patel. This is an open access article distributed under the Creative Commons Attribution License.
