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| Soft Prompt Learning with Internal Knowledge Expansion for Clickbait Detection |
| DONG Bingbing1,2, WU Xindong1,2 |
1. Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education of China, Hefei University of Technology, Hefei 230009;
2. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601 |
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Abstract The main purpose of clickbait is to increase page views and advertising revenues by enticing users to click on bait links. The content of clickbait is often characterized by low-quality, misleading or false information, and this potentially engenders negative effects on users. Existing prompt learning methods based on pre-trained language models are reliant on external open knowledge bases to detect clickbait. These methods not only limit model performance due to the quality and availability of external knowledge bases, but also inevitably lead to delays in queries and responses. To address this issue, a soft prompt learning method with internal knowledge expansion for clickbait detection(SPCD_IE) is proposed in this paper. Expansion words are extracted from the training dataset, while hierarchical clustering and optimization strategies are employed to fine-tune the obtained expansion words in prompt learning, and the necessity of knowledge retrieval from external knowledge bases is avoided. Moreover, soft prompt learning is utilized to obtain the best prompts suitable for specific text types, preventing biases introduced by manual templates. Although SPCD_IE expands solely based on internal knowledge in few-shot scenarios, experimental results show it achieves better detection performance on three public clickbait datasets in less time.
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Received: 08 May 2024
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| Fund:National Natural Science Foundation of China(No.62120106008) |
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Corresponding Authors:
WU XinDong, Ph.D., professor. His research interests include data mining, big data analysis and knowledge-based systems.
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| About author:: DONG Bingbing, Ph.D. candidate. Her research interests include data mining. |
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2024, Vol. 37 
