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With the explosive growth of online user-generated content and the desire by marketers to better utilize this space, it is beneficial to understand the viral diffusion of such content and to identify messages that are most likely to achieve popularity. In this paper, we combine network analysis and the diffusion literature to study the spreading of user-generated videos online. We identify three groups of factors that affect diffusion outcomes: network structure, content characteristics, and author characteristics. Using a proportional rates model, we analyze the diffusion of a sample of videos on YouTube. Our results show that it is preferable to have many subscribers who each has a few friends than to have a few subscribers with many connections. Furthermore, a curvilinear relationship exists between subscriber network connectivity and diffusion rate such that diffusion is at its highest under moderate connectivity. Examining content characteristics, we show that entertainment and educational values affect diffusion but production quality does not matter. Moreover, we find that quality as manifested by user ratings influences diffusion more than innate content quality. Not surprisingly, an author's past success carries over to the current content, and content from younger authors is more popular.
Liu–Thompkins et al. (Wed,) studied this question.