Information networks to derive value from social media

The rise in electronic interactions has made information networks ubiquitous. Correspondingly, research across multiple domains has begun to acknowledge the social and economic value of these networks for business decision-making. In this paper, the authors introduce a new type of information artifact, implicit brand networks, for obtaining close to real-time estimates of within-industry competition and across-industry complementarities. Statistical examination of the tacit links in the network, using Exponential Random Graph Models from network theory, reveals a mix of network and brand level characteristics responsible for the observed network structure. The paper concludes by discussing the practical applications of the information network, particularly for the automatic extraction of category-specific brand ratings. As information pertaining to category-specific ratings (e.g. sports, tech, luxury etc.) is rarely found in online users' comments, the brand network's ability to automatically reveal such insights, with minimal a-priori assumptions, is a significant contribution of this study.