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

"It is just a flu": Assessing the Effect of Watch History on YouTube's Pseudoscientific Video Recommendations