  YouTube is one of the most popular platforms for streaming of user-generated
video. Nowadays, professional YouTubers are organized in so called
multi-channel networks (MCNs). These networks offer services such as brand
deals, equipment, and strategic advice in exchange for a share of the
YouTubers' revenue. A major strategy to gain more subscribers and, hence,
revenue is collaborating with other YouTubers. Yet, collaborations on YouTube
have not been studied in a detailed quantitative manner. This paper aims to
close this gap with the following contributions. First, we collect a YouTube
dataset covering video statistics over three months for 7,942 channels. Second,
we design a framework for collaboration detection given a previously unknown
number of persons featuring in YouTube videos. We denote this framework for the
analysis of collaborations in YouTube videos using a Deep Neural Network (DNN)
based approach as CATANA. Third, we analyze about 2.4 years of video content
and use CATANA to answer research questions providing guidance for YouTubers
and MCNs for efficient collaboration strategies. Thereby, we focus on (i)
collaboration frequency and partner selectivity, (ii) the influence of MCNs on
channel collaborations, (iii) collaborating channel types, and (iv) the impact
of collaborations on video and channel popularity. Our results show that
collaborations are in many cases significantly beneficial in terms of viewers
and newly attracted subscribers for both collaborating channels, showing often
more than 100% popularity growth compared with non-collaboration videos.
