A Framework for Hate Speech Detection Using Deep Convolutional Neural Network
A Framework for Hate Speech Detection Using Deep Convolutional Neural Network
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DOI：
10.1109/access.2020.3037073
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发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Gao, Xiao-Zhi
中科院分区：
文献类型：
--
作者：
Roy, Pradeep Kumar;Tripathy, Asis Kumar;Gao, Xiao-Zhi
互联网用户的快速增长导致了不必要的网络问题，包括网络欺凌，仇恨言论等等。这篇文章涉及Twitter上的仇恨言论问题。仇恨言论似乎是一种煽动性的互动过程，利用误解来表达仇恨意识形态。仇恨言论侧重于各种受保护的方面，包括性别、宗教、种族和残疾。由于仇恨言论，有时当某人或一群人感到沮丧时，会发生不必要的犯罪。因此，有必要监控用户的帖子，并在传播之前过滤与仇恨言论相关的帖子。然而，Twitter每秒收到超过600条推文，每天收到约5亿条推文。从如此巨大的传入流量中手动过滤任何信息几乎是不可能的。关于这方面，使用深度卷积神经网络（DCNN）开发了一个自动化系统。本文提出的DCNN模型利用带有GloVe嵌入向量的推文文本，通过卷积运算来捕捉推文的语义，在最佳情况下，其准确率、召回率和F1得分值分别为0.97、0.88、0.92，优于现有模型。
The rapid growth of Internet users led to unwanted cyber issues, including cyberbullying, hate speech, and many more. This article deals with the problems of hate speech on Twitter. Hate speech appears to be an inflammatory kind of interaction process that uses misconceptions to express a hate ideology. The hate speech focuses on various protected aspects, including gender, religion, race, and disability. Owing to hate speech, sometimes unwanted crimes are going to happen as someone or a group of people get disheartened. Hence, it is essential to monitor user's posts and filter the hate speech related post before it is spread. However, Twitter receives more than six hundred tweets per second and about 500 million tweets per day. Manually filtering any information from such a huge incoming traffic is almost impossible. Concerning to this aspect, an automated system is developed using the Deep Convolutional Neural Network (DCNN). The proposed DCNN model utilises the tweet text with GloVe embedding vector to capture the tweets' semantics with the help of convolution operation and achieved the precision, recall and F1-score value as 0.97, 0.88, 0.92 respectively for the best case and outperformed the existing models.