Vaccine skepticism detection by network embedding

TL;DR

This paper applies network embedding techniques to classify vaccine skepticism on Twitter, analyzing user interactions and content to differentiate pro-vaxxer and vax-skeptic opinions during the COVID-19 pandemic.

Contribution

It introduces a scalable network embedding approach for detecting vaccine skepticism by analyzing Twitter interaction graphs and content.

Findings

Effective differentiation between pro-vaxxer and vax-skeptic groups.

Network embedding models scale well for large Twitter graphs.

Improved understanding of vaccine skepticism spread.

Abstract

We demonstrate the applicability of network embedding to vaccine skepticism, a controversial topic of long-past history. With the Covid-19 pandemic outbreak at the end of 2019, the topic is more important than ever. Only a year after the first international cases were registered, multiple vaccines were developed and passed clinical testing. Besides the challenges of development, testing, and logistics, another factor that might play a significant role in the fight against the pandemic are people who are hesitant to get vaccinated, or even state that they will refuse any vaccine offered to them. Two groups of people commonly referred to as a) pro-vaxxer, those who support vaccinating people b) vax-skeptic, those who question vaccine efficacy or the need for general vaccination against Covid-19. It is very difficult to tell exactly how many people share each of these views. It is even more difficult to understand all the reasoning why vax-skeptic opinions are getting more popular. In this work, our intention was to develop techniques that are able to efficiently differentiate between pro-vaxxer and vax-skeptic content. After multiple data preprocessing steps, we analyzed the tweet text as well as the structure of user interactions on Twitter. We deployed several node embedding and community detection models that scale well for graphs with millions of edges.