An Efficient Hybrid Deep Learning Approach for Detecting Online Abusive Language

TL;DR

This paper introduces a hybrid deep learning model combining BERT, CNN, and LSTM to effectively detect online abusive language across various platforms, achieving high accuracy even with imbalanced datasets.

Contribution

The paper presents a novel hybrid deep learning approach that integrates multiple architectures to improve detection of abusive language in diverse online environments.

Findings

Achieved approximately 99% accuracy and F1-score on a large, imbalanced dataset.

Effectively detects abusive content in diverse online platforms including dark web.

Captures semantic, contextual, and sequential patterns for robust detection.

Abstract

The digital age has expanded social media and online forums, allowing free expression for nearly 45% of the global population. Yet, it has also fueled online harassment, bullying, and harmful behaviors like hate speech and toxic comments across social networks, messaging apps, and gaming communities. Studies show 65% of parents notice hostile online behavior, and one-third of adolescents in mobile games experience bullying. A substantial volume of abusive content is generated and shared daily, not only on the surface web but also within dark web forums. Creators of abusive comments often employ specific words or coded phrases to evade detection and conceal their intentions. To address these challenges, we propose a hybrid deep learning model that integrates BERT, CNN, and LSTM architectures with a ReLU activation function to detect abusive language across multiple online platforms, including YouTube comments, online forum discussions, and dark web posts. The model demonstrates strong performance on a diverse and imbalanced dataset containing 77,620 abusive and 272,214 non-abusive text samples (ratio 1:3.5), achieving approximately 99% across evaluation metrics such as Precision, Recall, Accuracy, F1-score, and AUC. This approach effectively captures semantic, contextual, and sequential patterns in text, enabling robust detection of abusive content even in highly skewed datasets, as encountered in real-world scenarios.