Motivated by the rise of social media platforms that achieve a fusion of content and community, we consider the role of word-of-mouth communications (WOM) structured through a network. Using a data set from YouTube, we examine how cascades of WOM interactions enhance the popularity of videos. We first estimate the impact of channel influence and other network parameters in initiating WOM communications. The probit estimation considers the selection effect in videos that are likely to be associated with a greater propensity to trigger WOM. We find that factors related to a channel's ability to be a connector and a translator is most likely to result in the incidence of WOM. We then examine how cascades of WOM conversations have persistent impacts on subsequent video popularity. Empirically, the main issue here is heterogeneity in the epidemic potential of a video. Since the threshold might vary across videos, we use a finite mixture model. We also conduct a simultaneous estimation using latent instrumental variables to address endogeneity from unobservables. Our research has implications for researchers and practitioners by highlighting how WOM travels through networks of influence and susceptibility in disseminating awareness, and holds insights in regard to designing social recommendation systems and identifying trending topics in social media. > >
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.