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[2602.20634] Enhancing Hate Speech Detection on Social Media: A Comparative Analysis of Machine Learning Models and Text Transformation Approaches
Summary
This article evaluates various machine learning models for hate speech detection on social media, comparing traditional and advanced techniques, and introduces innovative text transformation methods to mitigate harmful content.
Why It Matters
With the rise of hate speech on social media, effective detection and moderation tools are crucial. This study provides insights into the performance of different machine learning models, highlighting their strengths and limitations, which can inform future developments in content moderation technologies.
Key Takeaways
- Advanced models like BERT show superior accuracy in detecting hate speech.
- Hybrid models combining different architectures can enhance detection capabilities.
- Innovative text transformation techniques can neutralize harmful expressions.
- The study highlights the strengths and limitations of current hate speech detection technologies.
- Future research directions are proposed for improving detection systems.
Computer Science > Computation and Language arXiv:2602.20634 (cs) [Submitted on 24 Feb 2026] Title:Enhancing Hate Speech Detection on Social Media: A Comparative Analysis of Machine Learning Models and Text Transformation Approaches Authors:Saurabh Mishra, Shivani Thakur, Radhika Mamidi View a PDF of the paper titled Enhancing Hate Speech Detection on Social Media: A Comparative Analysis of Machine Learning Models and Text Transformation Approaches, by Saurabh Mishra and 2 other authors View PDF Abstract:The proliferation of hate speech on social media platforms has necessitated the development of effective detection and moderation tools. This study evaluates the efficacy of various machine learning models in identifying hate speech and offensive language and investigates the potential of text transformation techniques to neutralize such content. We compare traditional models like CNNs and LSTMs with advanced neural network models such as BERT and its derivatives, alongside exploring hybrid models that combine different architectural features. Our results indicate that while advanced models like BERT show superior accuracy due to their deep contextual understanding, hybrid models exhibit improved capabilities in certain scenarios. Furthermore, we introduce innovative text transformation approaches that convert negative expressions into neutral ones, thereby potentially mitigating the impact of harmful content. The implications of these findings are discussed, highlighting ...