An Efficient Hybrid Deep Learning Approach for Detecting Online Abusive Language
A new model combining BERT, CNN, and LSTM achieves near-perfect detection on a dataset of 350,000 online comments.
A team of researchers has published a new paper proposing a highly effective hybrid deep learning model for detecting abusive language online. The model, developed by Vuong M. Ngo, Cach N. Dang, Kien V. Nguyen, and Mark Roantree, strategically combines three powerful architectures: BERT for understanding semantic context, CNN for extracting local features, and LSTM for capturing sequential dependencies in text. This integrated approach is specifically designed to handle the complex tactics used by bad actors, such as coded phrases and evasive language, across platforms from YouTube comments to dark web forums.
The model's performance is particularly notable given the challenging, real-world dataset it was tested on. The researchers used a large collection of 349,834 text samples, with a significant class imbalance (a 1:3.5 ratio of abusive to non-abusive content) that mirrors the skewed distribution found in actual online environments. Despite this imbalance, the hybrid model demonstrated remarkable robustness, achieving scores of approximately 99% across critical evaluation metrics including Precision, Recall, Accuracy, F1-score, and AUC (Area Under the Curve). This indicates a very low rate of both false positives and false negatives.
The research addresses a pressing global issue, citing studies that show 65% of parents observe hostile online behavior and one-third of adolescents experience bullying in mobile gaming communities. By providing a tool that can automatically and accurately flag abusive content at scale—even when creators try to conceal their intent—this work offers a potential technological foundation for safer digital spaces. The paper, titled 'An Efficient Hybrid Deep Learning Approach for Detecting Online Abusive Language,' is available on arXiv under the identifier arXiv:2603.09984.
- Hybrid architecture combines BERT, CNN, and LSTM to capture semantic, contextual, and sequential text patterns for robust detection.
- Achieved ~99% accuracy on a large, imbalanced real-world dataset of nearly 350,000 samples from platforms like YouTube and dark web forums.
- Specifically designed to detect evasive abusive language, including coded phrases, which standard models often miss.
Why It Matters
Provides a highly accurate, automated tool for platforms to combat harassment and hate speech at scale, creating safer online environments.