We study the drivers of the emergence of opinion leaders in a networked community where users establish links to others, indicating their "trust" for the link receiver's opinion. This leads to the formation of a network, with high in-degree individuals being the opinion leaders. We use a dyad-level proportional hazard model with time-varying covariates to model the growth of this network. To estimate our model, we use Weighted Exogenous Sampling with Bayesian Inference, a methodology that we develop for fast estimation of dyadic models on large network data sets. We find that, in the Epinions network, both the widely studied "preferential attachment" effect based on the existing number of inlinks (i.e., a network-based property of a node) and the number and quality of reviews written (i.e., an intrinsic property of a node) are significant drivers of new incoming trust links to a reviewer (i.e., inlinks to a node). Interestingly, we find that time is an important moderator of these effects—intrinsic node characteristics are a stronger short-term driver of additional inlinks, whereas the preferential attachment effect has a smaller impact but it persists for a longer time. Our novel insights have important managerial implications for the design of online review communities.
Lu, Yingda, Kinshuk Jerath, and Param Singh. "The Emergence of Opinion Leaders in a Networked Online Community: A Dyadic Model with Time Dynamics and a Heuristic for Fast Estimation." Management Science 59, no. 8 (August 2013): 1783-1799.
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