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

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