论文标题
Twitter行为的滥用语言检测和表征
Abusive Language Detection and Characterization of Twitter Behavior
论文作者
论文摘要
在这项工作中，使用双向复发神经网络（BIRNN）方法进行在线内容中的滥用语言检测。在这里，主要目标是关注Twitter上各种形式的虐待行为，并检测语音是否滥用。比较了社交媒体中各种虐待行为的结果，卷积神经NetWrok（CNN）和经常性神经网络（RNN）方法，并证明了拟议的Birnn是一种更好的深度学习模型，用于自动虐待语音检测。
In this work, abusive language detection in online content is performed using Bidirectional Recurrent Neural Network (BiRNN) method. Here the main objective is to focus on various forms of abusive behaviors on Twitter and to detect whether a speech is abusive or not. The results are compared for various abusive behaviors in social media, with Convolutional Neural Netwrok (CNN) and Recurrent Neural Network (RNN) methods and proved that the proposed BiRNN is a better deep learning model for automatic abusive speech detection.
