An Efficient Hybrid Deep Learning Approach for Detecting Online Abusive Language
arXiv:2603.09984v1 Announce Type: new 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, an
arXiv:2603.09984v1 Announce Type: new 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.
Executive Summary
This article proposes a hybrid deep learning model for detecting online abusive language, integrating BERT, CNN, and LSTM architectures with a ReLU activation function. The model demonstrates strong performance on a diverse and imbalanced dataset, achieving approximately 99% across evaluation metrics. The approach effectively captures semantic, contextual, and sequential patterns in text, enabling robust detection of abusive content even in highly skewed datasets. This model has significant implications for online safety and could be deployed across various online platforms to mitigate the spread of hate speech and toxic comments.
Key Points
- ▸ The proposed hybrid deep learning model integrates BERT, CNN, and LSTM architectures to detect online abusive language.
- ▸ The model demonstrates strong performance on a diverse and imbalanced dataset, achieving approximately 99% across evaluation metrics.
- ▸ The approach effectively captures semantic, contextual, and sequential patterns in text, enabling robust detection of abusive content.
Merits
Strength in Addressing Real-World Scenarios
The model's ability to effectively detect abusive content in highly skewed datasets encountered in real-world scenarios is a significant merit.
Improved Detection Accuracy
The model's strong performance across evaluation metrics, particularly its high precision and recall rates, is a notable merit.
Potential for Deployment Across Online Platforms
The model's potential for deployment across various online platforms, including social media, online forums, and gaming communities, is a significant merit.
Demerits
Limited Consideration of Human Factors
The model's reliance on machine learning algorithms and lack of consideration for human factors, such as context and intent, is a limitation.
Potential for Bias in Training Data
The model's performance may be influenced by bias in the training data, which could lead to inaccurate or unfair detection of abusive content.
Need for Continuous Model Updates
The model's performance may degrade over time as new forms of abusive language emerge, requiring continuous updates and refinements.
Expert Commentary
The proposed hybrid deep learning model demonstrates significant promise for detecting online abusive language, but its limitations and potential biases must be carefully considered. Furthermore, the model's implications for online safety and social media regulation require ongoing evaluation and refinement to ensure its effective deployment and maintenance.
Recommendations
- ✓ Further research is needed to address the model's limitations and potential biases, including the need for more diverse and representative training data.
- ✓ Policymakers and industry leaders should prioritize the development of effective strategies for addressing online harassment and bullying, leveraging the potential of technology to support these efforts.