A Comparative Study of Hybrid Models in Health Misinformation Text Classification
<|reference_start|>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.<|reference_end|>
@article{sikosana2024a,
    title={A Comparative Study of Hybrid Models in Health Misinformation Text
  Classification},
    author={Mkululi Sikosana, Oluwaseun Ajao and Sean Maudsley-Barton},
    journal={In Proceedings of the 4th International Workshop on Open
  Challenges in Online Social Networks (pp. 18-25) 2024},
    year={2024},
    doi={10.1145/3677117.3685007},
    archivePrefix={arXiv},
    eprint={2410.06311},
    primaryClass={cs.IR cs.AI cs.LG}
}