Detecting Anti-vaccine Users on Twitter


  • Matheus Schmitz University of Southern California, Information Sciences Institute
  • Goran Muric University of Southern California, Information Sciences Institute
  • Keith Burghardt University of Southern California, Information Sciences Institute



Social network analysis; communities identification; expertise and authority discovery, Text categorization; topic recognition; demographic/gender/age identification, Qualitative and quantitative studies of social media, Web and Social Media


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.




How to Cite

Schmitz, M., Muric, G., & Burghardt, K. (2023). Detecting Anti-vaccine Users on Twitter. Proceedings of the International AAAI Conference on Web and Social Media, 17(1), 787-795.