Influentials or Susceptibles? Analyzing Cascades of Word-of-Mouth Conversations in Online Social Networks

Abstract

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 dataset 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 provides implications for researchers and practitioners by highlighting how WOM travels through networks of influence and susceptibility in disseminating awareness, and holds out insights to the design of social recommendation systems and in identifying trending topics in social media.