标题： 基于详尽可能性模型（ELM）的混合CNN-LSTM模型在高级健康错误信息检测中的应用 显示英文标题

标题： Advanced Health Misinformation Detection Through Hybrid CNN-LSTM Models Informed by the Elaboration Likelihood Model (ELM)

摘要： 健康错误信息在新冠疫情中对全球公共卫生工作构成了重大挑战。 本研究应用详尽可能性模型（ELM）来增强社交媒体上的错误信息检测，使用了混合卷积神经网络（CNN）和长短期记忆（LSTM）模型。 该模型通过整合基于ELM的特征，如文本可读性、情感极性以及启发式线索（例如标点符号频率），以提高错误信息分类的检测准确性和可靠性。 增强的模型达到了97.37%的准确率，96.88%的精确率，98.50%的召回率，97.41%的F1分数，以及99.50%的ROC-AUC。 结合特征工程的模型进一步提高了性能，达到了98.88%的精确率，99.80%的召回率，99.41%的F1分数，以及99.80%的ROC-AUC。 这些发现突显了ELM特征在提高检测性能方面的价值，提供了有价值的上下文信息。 本研究展示了心理学理论在开发先进机器学习算法以有效应对健康错误信息方面的实际应用。 显示英文摘要

摘要： Health misinformation during the COVID-19 pandemic has significantly challenged public health efforts globally. This study applies the Elaboration Likelihood Model (ELM) to enhance misinformation detection on social media using a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model. The model aims to enhance the detection accuracy and reliability of misinformation classification by integrating ELM-based features such as text readability, sentiment polarity, and heuristic cues (e.g., punctuation frequency). The enhanced model achieved an accuracy of 97.37%, precision of 96.88%, recall of 98.50%, F1-score of 97.41%, and ROC-AUC of 99.50%. A combined model incorporating feature engineering further improved performance, achieving a precision of 98.88%, recall of 99.80%, F1-score of 99.41%, and ROC-AUC of 99.80%. These findings highlight the value of ELM features in improving detection performance, offering valuable contextual information. This study demonstrates the practical application of psychological theories in developing advanced machine learning algorithms to address health misinformation effectively.