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Symmetry 2018, 10(5), 138;

Clickbait Convolutional Neural Network

Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, Guangdong, China
School of Computing Science and Engineering, VIT University, Vellore 632014, India
Giiso Information Technology Co., Ltd., Shenzhen 518055, Guangdong, China
Author to whom correspondence should be addressed.
Received: 31 March 2018 / Revised: 25 April 2018 / Accepted: 27 April 2018 / Published: 1 May 2018
(This article belongs to the Special Issue Novel Machine Learning Approaches for Intelligent Big Data)
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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. View Full-Text
Keywords: clickbait detection; convolutional neural network; deep learning clickbait detection; convolutional neural network; deep learning

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Zheng, H.-T.; Chen, J.-Y.; Yao, X.; Sangaiah, A.K.; Jiang, Y.; Zhao, C.-Z. Clickbait Convolutional Neural Network. Symmetry 2018, 10, 138.

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