Modeling Co-Engagement Patterns in Brand Information Networks

The rise in electronic interactions has made information networks ubiquitous. Correspondingly, research across multiple domains has begun to explore the social and economic value of information networks for business decision-making. While most existing research focuses on descriptive and predictive properties of information networks, statistical analysis of the 'generative features' of information networks has largely been overlooked. The objective of our study is to create large-scale brand information networks, from common followership data on Twitter, and to model the generative features of the observed network structures. We propose to employ Exponential Random Graph Models to reveal a mix of network and individual level brand characteristics responsible for the formation of links between brands. Since links between brands arise from the aggregated interest patterns of Twitter users, the ERGM model essentially reveals brand and network characteristics associated with high user co-engagement patterns on social media.