Addressing Vaccine Misinformation on Social Media by leveraging Transformers and User Association Dynamics.

Vaccine hesitancy is a growing concern in public health, with increasing numbers of individuals expressing skepticism or outright refusal to receive vaccines. This factor was significantly highlighted during the COVID-19 pandemic, with large populations refusing to take the vaccine and prolonging the pandemic. This paper presents and compares two transformer-based approaches i.e. XLNET and BERT to classify vaccine misinformation on Twitter using the standard COVID-19 ANTi-Vax dataset. Subsequently, an analysis of vaccine discourse on Reddit is carried out following a user association mapping algorithm. The resultant graph was subsequently analyzed. The XLNET model outperformed BERT by showing a high accuracy of 0.9484, with an F1 score of 0.9353. The methodology can be used in multiple other scenarios to address concerns with regard to the usage of social media by analyzing network interactions. [ABSTRACT FROM AUTHOR]