大模型知识增强的多尺度图神经网络中文健康谣言识别研究

[Objective] To enable early detection of rumors, this study investigates rumor detection models and methods based on text content. [Methods] A Chinese health rumor detection model using multi-scale graph neural networks enhanced by large language models is proposed. First, a text graph is constructed for each individual document to capture latent information in the sentence. Second, entity information is extracted from the text through prompt engineering to enhance knowledge representation. Finally, a multi-scale graph neural network with feature decomposition is employed to perform rumor detection. [Results] The proposed model achieves macro F1 scores of 95.21% and 87.39% on the CHECKED and LTCR datasets, respectively, outperforming existing baseline models. [Limitations] The proposed model relies solely on textual input and dose not incorporate multimodal data such as images or videos. [Conclusions] Utilizing large language models for knowledge enhancement not only facilitates efficient and accurate entity extraction but also enrich sentence semantic representations. The integration of multi-scale graph neural networks with feature decomposition enable effective capture of hierarchical features without compromising computational stability. Constructing individual text graph per document enhances flexibility and adaptability in downstream applications. By combining these components, the overall performance of the model is significantly improved.