Enhancing Hate Speech Detection on Social Media: A Comparative Analysis of Machine Learning Models and Text Transformation Approaches
arXiv:2602.20634v1 Announce Type: new 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 f
arXiv:2602.20634v1 Announce Type: new 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 the strengths and limitations of current technologies and proposing future directions for more robust hate speech detection systems.
Executive Summary
The article evaluates various machine learning models for hate speech detection on social media, comparing traditional models like CNNs and LSTMs with advanced neural networks like BERT. The study also explores text transformation techniques to neutralize hate speech. The results show that advanced models like BERT exhibit superior accuracy, while hybrid models demonstrate improved capabilities in certain scenarios. The study introduces innovative text transformation approaches that can potentially mitigate the impact of harmful content, highlighting the strengths and limitations of current technologies and proposing future directions for more robust hate speech detection systems.
Key Points
- ▸ Comparison of traditional and advanced machine learning models for hate speech detection
- ▸ Evaluation of text transformation techniques to neutralize hate speech
- ▸ Introduction of hybrid models that combine different architectural features
Merits
Improved Accuracy
The use of advanced neural network models like BERT shows superior accuracy in detecting hate speech due to their deep contextual understanding.
Innovative Text Transformation
The introduction of text transformation approaches that convert negative expressions into neutral ones can potentially mitigate the impact of harmful content.
Demerits
Limited Contextual Understanding
Traditional models like CNNs and LSTMs may lack the deep contextual understanding required to accurately detect hate speech in certain scenarios.
Potential Biases
The use of machine learning models for hate speech detection may perpetuate existing biases if the training data is not diverse and representative.
Expert Commentary
The article provides a comprehensive evaluation of machine learning models for hate speech detection, highlighting the strengths and limitations of current technologies. The introduction of text transformation approaches is a significant innovation, offering a potential solution for mitigating the impact of harmful content. However, the study also raises important questions about the potential biases and limitations of machine learning models, emphasizing the need for ongoing research and development in this area. As social media platforms continue to grapple with the challenges of online content moderation, the development of more robust hate speech detection systems is crucial for promoting a safer and more respectful online environment.
Recommendations
- ✓ Further research is needed to develop more advanced machine learning models that can accurately detect hate speech in diverse linguistic and cultural contexts.
- ✓ Social media platforms should prioritize the development of transparent and accountable hate speech detection systems, incorporating human oversight and review to minimize the risk of errors or biases.
