A comparative study of hybrid models in health misinformation text classification

Sikosana, Mkululi, Ajao, Oluwaseun ORCID: https://orcid.org/0000-0002-6606-6569 and Maudsley-Barton, Sean ORCID: https://orcid.org/0000-0003-0289-0783 (2024) A comparative study of hybrid models in health misinformation text classification. In: OASIS 2024 : 4th International Workshop on Open Challenges in Online Social Networks (OASIS), 9 September 2024 - 13 September 2024, Poznan, Poland.

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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.