An Attention-Based Neural Network Using Human Semantic Knowledge and Its Application to Clickbait Detection
Clickbait is a commonly used social engineering technique to carry out phishing attacks, illegitimate marketing, and dissemination of disinformation. As a result, clickbait detection has become a popular research topic in recent years due to the prevalence of clickbait on the web and social media. I...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
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| Series: | IEEE Open Journal of the Computer Society |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9917322/ |
| _version_ | 1828156460083707904 |
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| author | Feng Wei Uyen Trang Nguyen |
| author_facet | Feng Wei Uyen Trang Nguyen |
| author_sort | Feng Wei |
| collection | DOAJ |
| description | Clickbait is a commonly used social engineering technique to carry out phishing attacks, illegitimate marketing, and dissemination of disinformation. As a result, clickbait detection has become a popular research topic in recent years due to the prevalence of clickbait on the web and social media. In this article, we propose a novel attention-based neural network for the task of clickbait detection. To the best of our knowledge, our work is the first that incorporates human semantic knowledge into an artificial neural network, and uses linguistic knowledge graphs to guide attention mechanisms for the clickbait detection task. Extensive experimental results show that the proposed model outperforms existing state-of-the-art clickbait classifiers, even when training data is limited. The proposed model also performs better or comparably to powerful pretrained models, namely, BERT, RoBERTa, and XLNet, while being much more lightweight. Furthermore, we conducted experiments to demonstrate that the use of human semantic knowledge can significantly enhance the performance of pretrained models in the semisupervised domain such as BERT, RoBERTa, and XLNet. |
| first_indexed | 2024-04-11T23:11:31Z |
| format | Article |
| id | doaj.art-ee956c34993842fd9ee30b3ed7bb0d77 |
| institution | Directory Open Access Journal |
| issn | 2644-1268 |
| language | English |
| last_indexed | 2024-04-11T23:11:31Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Computer Society |
| spelling | doaj.art-ee956c34993842fd9ee30b3ed7bb0d772022-12-22T03:57:49ZengIEEEIEEE Open Journal of the Computer Society2644-12682022-01-01321723210.1109/OJCS.2022.32137919917322An Attention-Based Neural Network Using Human Semantic Knowledge and Its Application to Clickbait DetectionFeng Wei0https://orcid.org/0000-0002-9993-8722Uyen Trang Nguyen1Department of Electrical Engineering and Computer Science, York University, Toronto, Ontario, CanadaDepartment of Electrical Engineering and Computer Science, York University, Toronto, Ontario, CanadaClickbait is a commonly used social engineering technique to carry out phishing attacks, illegitimate marketing, and dissemination of disinformation. As a result, clickbait detection has become a popular research topic in recent years due to the prevalence of clickbait on the web and social media. In this article, we propose a novel attention-based neural network for the task of clickbait detection. To the best of our knowledge, our work is the first that incorporates human semantic knowledge into an artificial neural network, and uses linguistic knowledge graphs to guide attention mechanisms for the clickbait detection task. Extensive experimental results show that the proposed model outperforms existing state-of-the-art clickbait classifiers, even when training data is limited. The proposed model also performs better or comparably to powerful pretrained models, namely, BERT, RoBERTa, and XLNet, while being much more lightweight. Furthermore, we conducted experiments to demonstrate that the use of human semantic knowledge can significantly enhance the performance of pretrained models in the semisupervised domain such as BERT, RoBERTa, and XLNet.https://ieeexplore.ieee.org/document/9917322/Clickbait detectionfake newshuman semantic knowledgeknowledge baseneural networks |
| spellingShingle | Feng Wei Uyen Trang Nguyen An Attention-Based Neural Network Using Human Semantic Knowledge and Its Application to Clickbait Detection IEEE Open Journal of the Computer Society Clickbait detection fake news human semantic knowledge knowledge base neural networks |
| title | An Attention-Based Neural Network Using Human Semantic Knowledge and Its Application to Clickbait Detection |
| title_full | An Attention-Based Neural Network Using Human Semantic Knowledge and Its Application to Clickbait Detection |
| title_fullStr | An Attention-Based Neural Network Using Human Semantic Knowledge and Its Application to Clickbait Detection |
| title_full_unstemmed | An Attention-Based Neural Network Using Human Semantic Knowledge and Its Application to Clickbait Detection |
| title_short | An Attention-Based Neural Network Using Human Semantic Knowledge and Its Application to Clickbait Detection |
| title_sort | attention based neural network using human semantic knowledge and its application to clickbait detection |
| topic | Clickbait detection fake news human semantic knowledge knowledge base neural networks |
| url | https://ieeexplore.ieee.org/document/9917322/ |
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