  The role played by YouTube's recommendation algorithm in unwittingly
promoting misinformation and conspiracy theories is not entirely understood.
Yet, this can have dire real-world consequences, especially when
pseudoscientific content is promoted to users at critical times, such as the
COVID-19 pandemic. In this paper, we set out to characterize and detect
pseudoscientific misinformation on YouTube. We collect 6.6K videos related to
COVID-19, the Flat Earth theory, as well as the anti-vaccination and anti-mask
movements. Using crowdsourcing, we annotate them as pseudoscience, legitimate
science, or irrelevant and train a deep learning classifier to detect
pseudoscientific videos with an accuracy of 0.79.
  We quantify user exposure to this content on various parts of the platform
and how this exposure changes based on the user's watch history. We find that
YouTube suggests more pseudoscientific content regarding traditional
pseudoscientific topics (e.g., flat earth, anti-vaccination) than for emerging
ones (like COVID-19). At the same time, these recommendations are more common
on the search results page than on a user's homepage or in the recommendation
section when actively watching videos. Finally, we shed light on how a user's
watch history substantially affects the type of recommended videos.
