Abstract
Health misinformation detection is a challenging but urgent problem in the field of information governance. In recent years, some studies have utilized long-form text detection models for this task, producing some promising early results. However, we found that most health information online is a short text, especially knowledge-based information. Meanwhile, the explainability of detection results is as important as the detection accuracy. There is no appropriate explainable short health misinformation detection model currently. To address these issues, we propose a novel Knowledge Enabled Short HEalth Misinformation detection framework, called KESHEM. This method extracts abundant knowledge from multiple, multi-form, and dynamically updated knowledge graphs (KGs) as supplementary material and effectively represents semantic features of the information contents and the external knowledge by powerful language models. KG-attention is then applied to distinguish the effects of each external knowledge for the information credibility reasoning and enhance the model’s explainability. We build a credible Chinese short text dataset for better evaluation and future research. Extensive experiments demonstrate that KESHEM significantly outperforms competing methods and accurately identifies important knowledge that explains the veracity of short health information.
This work is supported by the Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China under grant No. 21XNL018.
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The work is based solely on public data, with no privacy implications. Our data came from Chinese rumor-refuting platforms, where data is publicly available. Thus, we have no ethical violation in the collection data and experiment in our study. In addition, the detection results of health misinformation can only serve as a preliminary assessment and support, and for serious scenarios, experienced experts are required to make further assessments.
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Liu, F., Li, Y., Zuo, M. (2023). KESHEM: Knowledge Enabled Short Health Misinformation Detection Framework. In: Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14169. Springer, Cham. https://doi.org/10.1007/978-3-031-43412-9_22
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DOI: https://doi.org/10.1007/978-3-031-43412-9_22
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