Abstract
We develop a dynamic matched sample estimation algorithm to distinguish peer influence and homophily effects on item adoption decisions in dynamic networks, with numerous items diffusing simultaneously. We infer preferences using a machine learning algorithm applied to previous adoption decisions, and we match agents using those inferred preferences. We show that ignoring previous adoption decisions leads to significantly overestimating the role of peer influence in the diffusion of information, mistakenly confounding influence-based contagion with diffusion driven by common preferences. Our matching-on-preferences algorithm with machine learning reduces the relative effect of peer influence on item adoption decisions in this network significantly more than matching on earlier adoption decisions, as well other observable characteristics. We also show significant and intuitive heterogeneity in the relative effect of peer influence.
Highlights
In recent years, social scientists have become increasingly interested in the datasets generated by people interacting in online networks
We present a statistical framework for estimating the effect of peer influence on item adoption decisions in large dynamic online networks
Our results show that the effect of peer influence on item adoption decisions, relative to common preferences, is much smaller than it would appear to be if we do not control for past adoption behaviors
Summary
Social scientists have become increasingly interested in the datasets generated by people interacting in online networks. When studying these networks, an important issue is to estimate whether and how individuals’ decisions are affected by the decisions of their peers, a concept known as peer influence. Otherwise known as sorting or homophily, is the tendency of individuals to form and maintain links with those who are already like them [1], i.e. who have similar preferences. When we view behaviors or information spreading between connected individuals, we need to account for similar preferences when trying to estimate the impact of peer influence on contagion
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