Collaborations on YouTube: From Unsupervised Detection to the Impact on Video and Channel Popularity.

YouTube is the most popular platform for streaming of user-generated videos. Nowadays, professional YouTubers are organized in so-called multichannel networks (MCNs). These networks offer services such as brand deals, equipment, and strategic advice in exchange for a share of the YouTubers’ revenues. A dominant 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. To close this gap, first, we collect a YouTube dataset covering video statistics over 3 months for 7,942 channels. Second, we design a framework for collaboration detection given a previously unknown number of persons featured in YouTube videos. We denote this framework, for the detection and 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 guiding YouTubers and MCNs for efficient collaboration strategies. Thereby, we focus on (1) collaboration frequency and partner selectivity, (2) the influence of MCNs on channel collaborations, (3) collaborating channel types, and (4) the impact of collaborations on video and channel popularity. Our results show that collaborations are in many cases significantly beneficial regarding viewers and newly attracted subscribers for both collaborating channels, often showing more than 100% popularity growth compared with noncollaboration videos.