A Comparative Study of Hybrid Models in Health Misinformation Text Classification

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.

;Comment: 8 pages, 4 tables presented at the OASIS workshop of the ACM Hypertext and Social Media Conference 2024