# A Comparative Study of Hybrid Models in Health Misinformation Text Classification

arXiv: https://arxiv.org/abs/2410.06311 · HTML: https://mlsanity.com/paper/2410.06311/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 35 references that are in the map.
- First posted on arXiv 8 Oct 2024.

## Builds on (most cited first, 2 of 2)

- [Receiver operating characteristic (ROC) movies, universal ROC (UROC) curves, and coefficient of predictive ability (CPA)](https://mlsanity.com/paper/1912.01956/) (2019)
- [How Good Are SOTA Fake News Detectors](https://mlsanity.com/paper/2308.02727/) (2023)

## Built on by (most cited first, 5 of 5)

- [Linguistic Patterns in Pandemic-Related Content: A Comparative Analysis of COVID-19, Constraint, and Monkeypox Datasets](https://mlsanity.com/paper/2510.07579/) (2025)
- [A Multi-Branch Feature Fusion Approach for Health Misinformation Detection and Propagation](https://mlsanity.com/paper/2609.00403/) (2026)
- [A Structured Literature Review on Traditional Approaches in Current Natural Language Processing](https://mlsanity.com/paper/2505.12970/) (2025)
- [Integrating Persuasion Theory into the Epidemiological Modelling of Health Misinformation Spread on Social Media](https://mlsanity.com/paper/2608.15689/) (2026)
- [BERTopic-Virality Prioritisation: A Scalable Framework for Thematic and Comparative Analysis of COVID-19 and Monkeypox Misinformation on Twitter](https://mlsanity.com/paper/2608.15691/) (2026)

## Abstract (arXiv)

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.