Utilizing deep learning models for ternary classification in COVID-19 infodemic detection.

OBJECTIVE

To address the complexities of distinguishing truth from falsehood in the context of the COVID-19 infodemic, this paper focuses on utilizing deep learning models for infodemic ternary classification detection.

METHODS

Eight commonly used deep learning models are employed to categorize collected records as true, false, or uncertain. These models include fastText, three models based on recurrent neural networks, two models based on convolutional neural networks, and two transformer-based models.

RESULTS

Precision, recall, and 1-score metrics for each category, along with overall accuracy, are presented to establish benchmark results. Additionally, a comprehensive analysis of the confusion matrix is conducted to provide insights into the models' performance.

CONCLUSION

Given the limited availability of infodemic records and the relatively modest size of the two tested data sets, models with pretrained embeddings or simpler architectures tend to outperform their more complex counterparts. This highlights the potential efficiency of pretrained or simpler models for ternary classification in COVID-19 infodemic detection and underscores the need for further research in this area.