  YouTube is an important source of news and entertainment worldwide, but the
scale makes it challenging to study the ideas and topics being discussed on the
platform. This paper presents new methods to discover and classify YouTube
channels which enable the analysis of communities and categories on the
platform using orders of magnitude more channels than have been used in
previous studies. Instead of using channel and video data as features for
classification as other researchers have, these methods use a self-supervised
learning approach that leverages the public subscription pages of commenters.
We test the classification method on the task of predicting the political lean
of YouTube news channels and find that it outperforms the previous best model
on the task. Further experiments also show that there are important advantages
to using commenter subscriptions to discover channels. The subscription data,
along with an iterative approach, is applied to discover, to our current
understanding, the most comprehensive set of English language socio-political
YouTube channels yet to be analyzed. We experiment with predicting more fine
grained political tags for channels using a previously annotated dataset and
find that our model performs better than the average individual human reviewer
for most of the top tags. This fine grained political tag model is then applied
to the newly discovered English language socio-political channels to create a
new dataset to analyze the amount of traffic going to different political
content. The data shows that some tags, such as "Partisan Right" and
"Conspiracy", are significantly under represented when looking only at the most
popular socio-political channels. Through the use of our methods, we are able
to get a much more accurate picture of the size of these communities on
YouTube.
