{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/collaborations-on-youtube-from-unsupervised","title":"Collaborations on YouTube: From Unsupervised Detection to the Impact on Video and Channel Popularity","arxiv_id":"1805.01887","date":"2018-05-01","proceeding":null,"authors":["Christian Koch","Moritz Lode","Denny Stohr","Amr Rizk","Ralf Steinmetz"],"abstract":"YouTube is one of the most popular platforms for streaming of user-generated\nvideo. Nowadays, professional YouTubers are organized in so called\nmulti-channel networks (MCNs). These networks offer services such as brand\ndeals, equipment, and strategic advice in exchange for a share of the\nYouTubers' revenue. A major strategy to gain more subscribers and, hence,\nrevenue is collaborating with other YouTubers. Yet, collaborations on YouTube\nhave not been studied in a detailed quantitative manner. This paper aims to\nclose this gap with the following contributions. First, we collect a YouTube\ndataset covering video statistics over three months for 7,942 channels. Second,\nwe design a framework for collaboration detection given a previously unknown\nnumber of persons featuring in YouTube videos. We denote this framework for the\nanalysis of collaborations in YouTube videos using a Deep Neural Network (DNN)\nbased approach as CATANA. Third, we analyze about 2.4 years of video content\nand use CATANA to answer research questions providing guidance for YouTubers\nand MCNs for efficient collaboration strategies. Thereby, we focus on (i)\ncollaboration frequency and partner selectivity, (ii) the influence of MCNs on\nchannel collaborations, (iii) collaborating channel types, and (iv) the impact\nof collaborations on video and channel popularity. Our results show that\ncollaborations are in many cases significantly beneficial in terms of viewers\nand newly attracted subscribers for both collaborating channels, showing often\nmore than 100% popularity growth compared with non-collaboration videos.","url_abs":"http://arxiv.org/abs/1805.01887v1","url_pdf":"http://arxiv.org/pdf/1805.01887v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"collaborations-on-youtube-from-unsupervised","repo_url":"https://github.com/christiannkoch/CATANA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}