Abstract
Vaccine hesitancy has a long history but has been recently driven by the anti-vaccine narratives shared online, which 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, partly because of this, became indecisive or unwilling to be vaccinated. The goal of this study is to better understand anti-vaccine sentiment, and work to reduce its impact, 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 spread anti-vaccine sentiment in the future. The software package is built using text embedding methods, neural networks, and automated dataset generation. It is trained on over one hundred thousand accounts and several million tweets. This model will help researchers and policy-makers understand anti-vaccine discussion and misinformation strategies, which can further help tailor targeted campaigns seeking to inform and debunk the harmful anti-vaccination myths currently being spread. Additionally, we leverage the data on such users to understand what are the moral and emotional characteristics of anti-vaccine spreaders.
Abstract (translated)
URL
https://arxiv.org/abs/2110.11333
