Design and Implementation of a Multichannel Convolutional Neural Network for Hate Speech Detection in Social Networks
Revue d'intelligence artificielle(2022)
摘要
As the number of social media comments available online grows, the spread of hate speech has grown gradually. When someone uses hate speech as a weapon to injure, degrade, and humiliate others, their freedom, dignity, and personhood can be jeopardized. Deep neural network-based hate speech detection models, such as the conventional single channel convolutional neural network (SC-CNN), have recently demonstrated promising results. The success of the models, however, is dependent on the type of language they are trained on and the training data size. Even with a small amount of training data, the model's performance can be improved by using a multichannel convolutional neural network (MC-CNN) model. The study assesses and compares the performance of a multichannel convolutional neural network model to single channel convolutional neural network models using a support vector machine (SVM) as a baseline. The models' F1 score values are computed, and promising results are obtained. The MC-CNN model outperforms the SC-CNN models in all three hate speech datasets. The study's findings indicate that the proposed MC-CNN model could be used as a deep learning-based alternative for hate speech detection.
更多查看译文
关键词
hate speech detection,multichannel convolutional neural network,neural network
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要