Clickbait Convolutional Neural Network
With the development of online advertisements, clickbait spread wider and wider. Clickbait dissatisfies users because the article content does not match their expectation. Thus, clickbait detection has attracted more and more attention recently. Traditional clickbait-detection methods rely on heavy...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-05-01
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| Series: | Symmetry |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2073-8994/10/5/138 |
| _version_ | 1811306673707417600 |
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| author | Hai-Tao Zheng Jin-Yuan Chen Xin Yao Arun Kumar Sangaiah Yong Jiang Cong-Zhi Zhao |
| author_facet | Hai-Tao Zheng Jin-Yuan Chen Xin Yao Arun Kumar Sangaiah Yong Jiang Cong-Zhi Zhao |
| author_sort | Hai-Tao Zheng |
| collection | DOAJ |
| description | With the development of online advertisements, clickbait spread wider and wider. Clickbait dissatisfies users because the article content does not match their expectation. Thus, clickbait detection has attracted more and more attention recently. Traditional clickbait-detection methods rely on heavy feature engineering and fail to distinguish clickbait from normal headlines precisely because of the limited information in headlines. A convolutional neural network is useful for clickbait detection, since it utilizes pretrained Word2Vec to understand the headlines semantically, and employs different kernels to find various characteristics of the headlines. However, different types of articles tend to use different ways to draw users’ attention, and a pretrained Word2Vec model cannot distinguish these different ways. To address this issue, we propose a clickbait convolutional neural network (CBCNN) to consider not only the overall characteristics but also specific characteristics from different article types. Our experimental results show that our method outperforms traditional clickbait-detection algorithms and the TextCNN model in terms of precision, recall and accuracy. |
| first_indexed | 2024-04-13T08:49:42Z |
| format | Article |
| id | doaj.art-3d9f71e60e4643d0a03bf3196c5da334 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-04-13T08:49:42Z |
| publishDate | 2018-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-3d9f71e60e4643d0a03bf3196c5da3342022-12-22T02:53:31ZengMDPI AGSymmetry2073-89942018-05-0110513810.3390/sym10050138sym10050138Clickbait Convolutional Neural NetworkHai-Tao Zheng0Jin-Yuan Chen1Xin Yao2Arun Kumar Sangaiah3Yong Jiang4Cong-Zhi Zhao5Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, Guangdong, ChinaGraduate School at Shenzhen, Tsinghua University, Shenzhen 518055, Guangdong, ChinaGraduate School at Shenzhen, Tsinghua University, Shenzhen 518055, Guangdong, ChinaSchool of Computing Science and Engineering, VIT University, Vellore 632014, IndiaGraduate School at Shenzhen, Tsinghua University, Shenzhen 518055, Guangdong, ChinaGiiso Information Technology Co., Ltd., Shenzhen 518055, Guangdong, ChinaWith the development of online advertisements, clickbait spread wider and wider. Clickbait dissatisfies users because the article content does not match their expectation. Thus, clickbait detection has attracted more and more attention recently. Traditional clickbait-detection methods rely on heavy feature engineering and fail to distinguish clickbait from normal headlines precisely because of the limited information in headlines. A convolutional neural network is useful for clickbait detection, since it utilizes pretrained Word2Vec to understand the headlines semantically, and employs different kernels to find various characteristics of the headlines. However, different types of articles tend to use different ways to draw users’ attention, and a pretrained Word2Vec model cannot distinguish these different ways. To address this issue, we propose a clickbait convolutional neural network (CBCNN) to consider not only the overall characteristics but also specific characteristics from different article types. Our experimental results show that our method outperforms traditional clickbait-detection algorithms and the TextCNN model in terms of precision, recall and accuracy.http://www.mdpi.com/2073-8994/10/5/138clickbait detectionconvolutional neural networkdeep learning |
| spellingShingle | Hai-Tao Zheng Jin-Yuan Chen Xin Yao Arun Kumar Sangaiah Yong Jiang Cong-Zhi Zhao Clickbait Convolutional Neural Network Symmetry clickbait detection convolutional neural network deep learning |
| title | Clickbait Convolutional Neural Network |
| title_full | Clickbait Convolutional Neural Network |
| title_fullStr | Clickbait Convolutional Neural Network |
| title_full_unstemmed | Clickbait Convolutional Neural Network |
| title_short | Clickbait Convolutional Neural Network |
| title_sort | clickbait convolutional neural network |
| topic | clickbait detection convolutional neural network deep learning |
| url | http://www.mdpi.com/2073-8994/10/5/138 |
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