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2025
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| Online Access: | https://doi.org/10.5281/zenodo.14837249 |
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| author | Dr. Yashaswini S, Dr. Jayanthi M G, Satya Bonthala, G Kavya Rao |
| author_facet | Dr. Yashaswini S, Dr. Jayanthi M G, Satya Bonthala, G Kavya Rao |
| contents | <p>Hate speech has become a growing concern in the <br>digital age due to its potential to harm individuals and <br>communities. With the increasing prevalence of social media and <br>online platforms, the detection of hate speech has become a <br>critical task for ensuring a safer online environment. Traditional <br>methods for identifying hate speech often rely on keyword <br>matching and manual moderation, which can be inefficient and <br>inaccurate. In this paper, we explore the application of deep <br>learning techniques for hate speech detection. By leveraging <br>advanced neural network architectures such as Convolutional <br>Neural Networks (CNNs), Recurrent Neural Networks (RNNs), <br>and Transformer- based models, we aim to achieve a high level of <br>accuracy in identifying hate speech across various datasets. Our <br>approach involves preprocessing text data, feature extraction, <br>and model training to handle linguistic nuances, contextual <br>dependencies, and varying levels of toxicity. Experimental results <br>demonstrate the efficacy of our proposed model in detecting hate <br>speech, outperforming traditional methods and providing a <br>robust solution to combat online hate speech effectively. This <br>research highlights the potential of deep learning as a powerful <br>tool for fostering safer digital communication. Keywords: Hate <br>Speech Detection, Deep Learning, Neural Networks, Text <br>Classification, Natural Language Processing, Machine Learning, <br>Transformer Models, Sentiment Analysis, Toxicity Analysis, <br>Text Preprocessing, Feature Extraction, Contextual Analysis, <br>and Online Safety</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_14837249 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
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| spellingShingle | Leveraging Deep Learning Techniques for Hate Speech Identification Dr. Yashaswini S, Dr. Jayanthi M G, Satya Bonthala, G Kavya Rao <p>Hate speech has become a growing concern in the <br>digital age due to its potential to harm individuals and <br>communities. With the increasing prevalence of social media and <br>online platforms, the detection of hate speech has become a <br>critical task for ensuring a safer online environment. Traditional <br>methods for identifying hate speech often rely on keyword <br>matching and manual moderation, which can be inefficient and <br>inaccurate. In this paper, we explore the application of deep <br>learning techniques for hate speech detection. By leveraging <br>advanced neural network architectures such as Convolutional <br>Neural Networks (CNNs), Recurrent Neural Networks (RNNs), <br>and Transformer- based models, we aim to achieve a high level of <br>accuracy in identifying hate speech across various datasets. Our <br>approach involves preprocessing text data, feature extraction, <br>and model training to handle linguistic nuances, contextual <br>dependencies, and varying levels of toxicity. Experimental results <br>demonstrate the efficacy of our proposed model in detecting hate <br>speech, outperforming traditional methods and providing a <br>robust solution to combat online hate speech effectively. This <br>research highlights the potential of deep learning as a powerful <br>tool for fostering safer digital communication. Keywords: Hate <br>Speech Detection, Deep Learning, Neural Networks, Text <br>Classification, Natural Language Processing, Machine Learning, <br>Transformer Models, Sentiment Analysis, Toxicity Analysis, <br>Text Preprocessing, Feature Extraction, Contextual Analysis, <br>and Online Safety</p> |
| title | Leveraging Deep Learning Techniques for Hate Speech Identification |
| url | https://doi.org/10.5281/zenodo.14837249 |