  Research using YouTube data often explores social and semantic dimensions of
channels and videos. Typically, analyses rely on laborious manual annotation of
content and content creators, often found by low-recall methods such as keyword
search. Here, we explore an alternative approach, using latent representations
(embeddings) obtained via machine learning. Using a large dataset of YouTube
links shared on Reddit; we create embeddings that capture social sharing
behavior, video metadata (title, description, etc.), and YouTube's video
recommendations. We evaluate these embeddings using crowdsourcing and existing
datasets, finding that recommendation embeddings excel at capturing both social
and semantic dimensions, although social-sharing embeddings better correlate
with existing partisan scores. We share embeddings capturing the social and
semantic dimensions of 44,000 YouTube channels for the benefit of future
research on YouTube: https://github.com/epfl-dlab/youtube-embeddings.
