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

TL;DR

This paper presents a hybrid CNN-LSTM model enhanced with Elaboration Likelihood Model features to detect health misinformation on social media with high accuracy, demonstrating the effectiveness of integrating psychological insights into machine learning.

Contribution

It introduces a novel hybrid CNN-LSTM approach informed by ELM features, significantly improving misinformation detection accuracy and reliability.

Findings

Achieved 97.37% accuracy in misinformation detection.

Enhanced model reached 98.88% precision and 99.80% recall.

Demonstrated the effectiveness of psychological features in machine learning models.

Abstract

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.