Deep learning techniques for hate speech detection

• Main Author: Han, Angel Feng Yi
• Other Authors: Luu Anh Tuan
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2023
• Subjects: Engineering::Computer science and engineering
• Online Access: https://hdl.handle.net/10356/165997
• Institution: Nanyang Technological University

Hate speech has become a persistent concern in internet communication channels. More people than ever before are able to express their ideas and opinions because of the growth of social media and other online platforms. Sadly, this increased freedom of speech has also given rise to provocative, hateful, and discriminating speech. Growing interest has been shown in creating automated tools for hate speech identification in order to address this issue. This project's main goal is to investigate deep learning methods for identifying hate speech in text. The project's specific goal is to look into modern deep learning architectures and datasets that can be used to address the issue. Also, the effectiveness of various algorithms and their accuracy in detecting hate speech will be evaluated. The objectives of the research will be achieved by utilizing the most recent deep learning frameworks and libraries, including PyTorch, TensorFlow, and Keras. Convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models like BERT are some common deep learning designs that will be examined in this research for their efficacy. Also, the performance of pre-trained language models will be compared and reviewed for improvement. The study will benefit the research community by offering a thorough examination of the most recent deep learning methods for the detection of hate speech. The results will offer insightful information regarding how well various models and pre-trained language models perform in this task. The study will aid in the creation of software that can automatically identify hate speech and stop it from being propagated online. The study can also aid in creating a more welcoming and secure online community.