Detecting Anti-Vaccine Users on Twitter

TL;DR

This paper presents a machine learning system that predicts anti-vaccine users on Twitter before they post anti-vaccine content, aiding understanding and intervention strategies.

Contribution

It introduces a novel Python package utilizing text embeddings and neural networks to identify anti-vaccine users proactively on social media.

Findings

Model detects anti-vaccine users up to a year in advance.

Text analysis reveals moral and emotional differences among users.

System trained on several million tweets with high accuracy.

Abstract

Vaccine hesitancy, which has recently been driven by online narratives, significantly degrades the efficacy of vaccination strategies, such as those for COVID-19. Despite broad agreement in the medical community about the safety and efficacy of available vaccines, a large number of social media users continue to be inundated with false information about vaccines and are indecisive or unwilling to be vaccinated. The goal of this study is to better understand anti-vaccine sentiment by developing a system capable of automatically identifying the users responsible for spreading anti-vaccine narratives. We introduce a publicly available Python package capable of analyzing Twitter profiles to assess how likely that profile is to share anti-vaccine sentiment in the future. The software package is built using text embedding methods, neural networks, and automated dataset generation and is trained on several million tweets. We find this model can accurately detect anti-vaccine users up to a year before they tweet anti-vaccine hashtags or keywords. We also show examples of how text analysis helps us understand anti-vaccine discussions by detecting moral and emotional differences between anti-vaccine spreaders on Twitter and regular users. Our results will help researchers and policy-makers understand how users become anti-vaccine and what they discuss on Twitter. Policy-makers can utilize this information for better targeted campaigns that debunk harmful anti-vaccination myths.