用心理模型提升社交媒体假健康信息识别准确率
Advanced Health Misinformation Detection Through Hybrid CNN-LSTM Models Informed by the Elaboration Likelihood Model (ELM)
- 结合心理学理论设计文本可读性、情绪极性等特征
- 模型最高达99.8%的召回率和99.5%的ROC-AUC
- 适合关注虚假信息检测与人机协同研究者
COVID-19疫情期间，健康类虚假信息对全球公共卫生构成严峻挑战。本研究基于详尽可能性模型（ELM）构建混合卷积神经网络（CNN）与长短期记忆网络（LSTM）模型，通过引入文本可读性、情感极性及启发式线索（如标点符号频率）等特征，提升虚假信息分类的准确性与可靠性。优化后模型在测试集上达到97.37%的准确率、96.88%的精确率、98.50%的召回率、97.41%的F1分数和99.50%的ROC-AUC。进一步融合特征工程的组合模型表现更优，精确率达98.88%，召回率达99.80%，F1分数为99.41%，ROC-AUC达99.80%。结果表明，基于ELM的特征显著提升检测性能，为应对健康类虚假信息提供了有效的技术路径。
原文摘要 · Abstract (English)
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.
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