The Emergence of Opinion Leaders in a Networked Online Community: A Dyadic Model with Time Dynamics and a Heuristic for Fast Estimation

Abstract:     
We study the drivers of the emergence of opinion leaders in a networked community where users share information with each other. Our specific setting is that of Epinions.com, a website dedicated to user-generated product reviews. Epinions.com employs a novel mechanism in which every member of the community can include other members, whose reviews she trusts, in her “web of trust.” This leads to the formation of a network of trust among reviewers with high in-degree individuals being the opinion leaders. Accordingly, we study the emergence of opinion leaders in this community using a network formation paradigm. We model network growth by using a dyad-level proportional hazard model with time-varying covariates. To estimate this model, we use Weighted Exogenous Sampling with Bayesian Inference (WESBI), a methodology that we develop for fast estimation of dyadic models on large network datasets.

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, time is an important moderator of these effects — the number of recent reviews written has a stronger effect than the effect of the number of recent inlinks received on the current rate of attracting inlinks; however, the aggregate number of reviews written in the past has no effect, while the aggregate number of inlinks obtained in the past has a significant effect on the current rate of attracting inlinks. This leads to the novel and important implication that, in a network growth setting, intrinsic node characteristics are a stronger short-term driver of additional inlinks, while the preferential attachment effect has a smaller impact but it persists for a longer time. We discuss the managerial implications of our results for the design and organization of online review communities.

The Online Technical Appendix for this paper is available at the following URL: http://ssrn.com/abstract=2206016

Number of Pages in PDF File: 33

Keywords: user-generated content, opinion leaders, social networks, network growth, proportional hazard model, weighted exogenous sampling

JEL Classification: M30, M31

Accepted Paper Series