  Vaccination rates are decreasing in many areas of the world, and outbreaks of
preventable diseases tend to follow in areas with particular low rates. Much
research has been devoted to improving our understanding of the motivations
behind vaccination decisions and the effects of various types of information
offered to skeptics, no large-scale study of the structure of online
vaccination discourse have been conducted.
  Here, we offer an approach to quantitatively study the vaccine discourse in
an online system, exemplified by Twitter. We use train a deep neural network to
predict tweet vaccine sentiments, surpassing state-of-the-art performance,
attaining two-class accuracy of $90.4\%$, and a three-class F1 of $0.762$. We
identify profiles which consistently produce strongly anti- and pro-vaccine
content. We find that strongly anti-vaccine profiles primarily post links to
Youtube, and commercial sites that make money on selling alternative health
products, representing a conflict of interest. We also visualize the network of
repeated mutual interactions of actors in the vaccine discourse and find that
it is highly stratified, with an assortativity coefficient of $r = .813$.
