F-DenseCNN: feature-based dense convolutional neural networks and swift text word embeddings for enhanced hate speech prediction.

Hate speech on social media platforms poses a significant threat to individuals and society, necessitating robust automated detection systems. While existing approaches employ supervised machine learning with text mining elements, they often fall short in capturing the nuanced and evolving nature of hate speech, including subtle linguistic cues, implicit biases, and coded language. This study addresses these limitations by introducing two novel techniques: the feature-based dense convolutional neural network and the swift text word embedding technique. Our key contributions include the development of F-DenseCNN, a deep learning architecture designed to extract complex features from textual data, and the introduction of the swift text word embedding technique, offering efficient and context-aware word representations. Extensive experimentation and evaluation demonstrate that our proposed method significantly outperforms conventional approaches, achieving a 96.2% accuracy in hate speech detection. This substantial improvement in detection accuracy has important implications for content moderation systems, potentially enhancing their reliability and effectiveness in combating online hate speech. Our findings underscore the potential of advanced deep learning techniques in addressing the evolving challenges of hate speech detection on social media platforms. [ABSTRACT FROM AUTHOR]