Social Networks and the Diffusion of User-Generated Content: Evidence from YouTube

TLDR

YouTube’s rapid content dissemination and highly variable video success create a networked environment where identifying social influence is challenged by reflection and heterogeneity problems. The study uses a unique YouTube dataset of video and user information to analyze how social interactions shape video success and its magnitude. The analysis shows that social interactions strongly determine which YouTube videos succeed and how much they succeed, with mechanisms such as conformity, homophily, and network‑guided discovery, and these findings differ from Bass‑model predictions, remain robust to self‑selection and heterogeneity, and have implications for researchers and managers.

Abstract

This paper is motivated by the success of YouTube, which is attractive to content creators as well as corporations for its potential to rapidly disseminate digital content. The networked structure of interactions on YouTube and the tremendous variation in the success of videos posted online lends itself to an inquiry of the role of social influence. Using a unique data set of video information and user information collected from YouTube, we find that social interactions are influential not only in determining which videos become successful but also on the magnitude of that impact. We also find evidence for a number of mechanisms by which social influence is transmitted, such as (i) a preference for conformity and homophily and (ii) the role of social networks in guiding opinion formation and directing product search and discovery. Econometrically, the problem in identifying social influence is that individuals' choices depend in great part upon the choices of other individuals, referred to as the reflection problem. Another problem in identification is to distinguish between social contagion and user heterogeneity in the diffusion process. Our results are in sharp contrast to earlier models of diffusion, such as the Bass model, that do not distinguish between different social processes that are responsible for the process of diffusion. Our results are robust to potential self-selection according to user tastes, temporal heterogeneity and the reflection problem. Implications for researchers and managers are discussed.