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Detecting fake information with knowledge-enhanced AutoPrompt

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Abstract

The rapid growth of fake news on the Internet poses a challenge to accessing authentic information. Fake news detection plays a crucial role in filtering out false information and improving information accuracy. However, practical implementation faces challenges like high annotation costs, limited samples, poor training results, and weak model generalization. To tackle these issues, we propose knowledge-enhanced AutoPrompt (KEAP) for fake news detection. This method leverages prompt templates generated by the T5 model to transform the fake news detection task into a prompt learning-based task. We also incorporate external entity knowledge to enhance detection capabilities. Carefully designed prompts activate the model’s latent knowledge, improving performance in low-resource scenarios and model generalization. Experiments on GossipCop and PolitiFact datasets demonstrate the superiority of prompt learning over existing methods without extra text during testing. KEAP achieves an average F1 score improvement of 2.79 \(\%\) compared to state-of-the-art methods in few-shot settings.

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Availability of data and materials

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This work was partially supported by the National Natural Science Foundation of China (Grant Nos. 620 72449,61802197) and is also funded in part by the Science and Technology Development Fund, Macau SAR (Grant no. 0018/2019/AKP and SKL-IOTSC(UM)-2021–2023), in part by the Guangdong Science and Technology Department (Grant no. 2018B030324002), in part by the Zhuhai Science and Technology Innovation Bureau Zhuhai-Hong Kong-Macau Special Cooperation Project (Grant no. ZH22017002200001PWC)

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, China

    Xun Che & Qianmu Li

  2. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Gang Yang & Yadang Chen

Authors
  1. Xun Che
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  2. Gang Yang
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  3. Yadang Chen
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  4. Qianmu Li
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Contributions

Xun Che was involved in the methodology, conceptualization, validation, writing—original draft, writing—review and editing. Gang Yang contributed to the methodology, investigation, data curation, and software. Yadang Chen assisted in writing—review and editing and supervision. Qianmu Li contributed to writing—review and editing and supervision

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Correspondence to Yadang Chen.

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Che, X., Yang, G., Chen, Y. et al. Detecting fake information with knowledge-enhanced AutoPrompt. Neural Comput & Applic 36, 7725–7742 (2024). https://doi.org/10.1007/s00521-024-09491-7

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