Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/sym10050138 | SYMMETRY-BASEL |
Keywords | Field | DocType |
clickbait detection,convolutional neural network,deep learning | Combinatorics,Convolutional neural network,Feature engineering,Artificial intelligence,Word2vec,Deep learning,Recall,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
10 | 5 | 2073-8994 |
Citations | PageRank | References |
6 | 0.50 | 6 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zheng Hai-Tao | 1 | 142 | 24.39 |
| Chen Jin-Yuan | 2 | 22 | 3.27 |
| Yao Xin | 3 | 6 | 0.50 |
| Arun Kumar | 4 | 1427 | 132.32 |
| Jiang Yong | 5 | 6 | 0.50 |
| Zhao Cong-Zhi | 6 | 6 | 2.19 |