A Comparative Study of Hybrid Models in Health Misinformation Text Classification

TL;DR

This study compares machine learning, deep learning, and hybrid models for detecting COVID-19 health misinformation on social media, finding that deep learning and hybrid models outperform traditional ML classifiers in accuracy and robustness.

Contribution

It provides a comprehensive evaluation of various ML and DL models, including hybrid architectures, on a COVID-19 misinformation dataset, highlighting the superior performance of DL and hybrid models.

Findings

DL models with Word2Vec embeddings exceed 98% in all metrics

Hybrid CNN+LSTM models outperform pretrained models like BERT and RoBERTa

SVM achieves a 94.41% F1-score in misinformation detection

Abstract

This study evaluates the effectiveness of machine learning (ML) and deep learning (DL) models in detecting COVID-19-related misinformation on online social networks (OSNs), aiming to develop more effective tools for countering the spread of health misinformation during the pan-demic. The study trained and tested various ML classifiers (Naive Bayes, SVM, Random Forest, etc.), DL models (CNN, LSTM, hybrid CNN+LSTM), and pretrained language models (DistilBERT, RoBERTa) on the "COVID19-FNIR DATASET". These models were evaluated for accuracy, F1 score, recall, precision, and ROC, and used preprocessing techniques like stemming and lemmatization. The results showed SVM performed well, achieving a 94.41% F1-score. DL models with Word2Vec embeddings exceeded 98% in all performance metrics (accuracy, F1 score, recall, precision & ROC). The CNN+LSTM hybrid models also exceeded 98% across performance metrics, outperforming pretrained models like DistilBERT and RoBERTa. Our study concludes that DL and hybrid DL models are more effective than conventional ML algorithms for detecting COVID-19 misinformation on OSNs. The findings highlight the importance of advanced neural network approaches and large-scale pretraining in misinformation detection. Future research should optimize these models for various misinformation types and adapt to changing OSNs, aiding in combating health misinformation.