A Framework for Hate Speech Detection Using Deep Convolutional Neural Network
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 ideol...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9253658/ |
| _version_ | 1818936300305121280 |
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| author | Pradeep Kumar Roy Asis Kumar Tripathy Tapan Kumar Das Xiao-Zhi Gao |
| author_facet | Pradeep Kumar Roy Asis Kumar Tripathy Tapan Kumar Das Xiao-Zhi Gao |
| author_sort | Pradeep Kumar Roy |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T05:33:52Z |
| format | Article |
| id | doaj.art-a79f4500bb8344fd96376b2e7ec5381f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T05:33:52Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a79f4500bb8344fd96376b2e7ec5381f2022-12-21T19:51:41ZengIEEEIEEE Access2169-35362020-01-01820495120496210.1109/ACCESS.2020.30370739253658A Framework for Hate Speech Detection Using Deep Convolutional Neural NetworkPradeep Kumar Roy0https://orcid.org/0000-0001-5513-2834Asis Kumar Tripathy1https://orcid.org/0000-0003-2685-9860Tapan Kumar Das2https://orcid.org/0000-0002-2683-3516Xiao-Zhi Gao3https://orcid.org/0000-0002-0078-5675School of Information Technology, Vellore Institute of Technology, Vellore, IndiaSchool of Information Technology, Vellore Institute of Technology, Vellore, IndiaSchool of Information Technology, Vellore Institute of Technology, Vellore, IndiaSchool of Computing, University of Eastern Finland, Kuopio, FinlandThe 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.https://ieeexplore.ieee.org/document/9253658/Convolutional neural networkhate speechLSTMTf-IdfTwitter |
| spellingShingle | Pradeep Kumar Roy Asis Kumar Tripathy Tapan Kumar Das Xiao-Zhi Gao A Framework for Hate Speech Detection Using Deep Convolutional Neural Network IEEE Access Convolutional neural network hate speech LSTM Tf-Idf |
| title | A Framework for Hate Speech Detection Using Deep Convolutional Neural Network |
| title_full | A Framework for Hate Speech Detection Using Deep Convolutional Neural Network |
| title_fullStr | A Framework for Hate Speech Detection Using Deep Convolutional Neural Network |
| title_full_unstemmed | A Framework for Hate Speech Detection Using Deep Convolutional Neural Network |
| title_short | A Framework for Hate Speech Detection Using Deep Convolutional Neural Network |
| title_sort | framework for hate speech detection using deep convolutional neural network |
| topic | Convolutional neural network hate speech LSTM Tf-Idf |
| url | https://ieeexplore.ieee.org/document/9253658/ |
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