Spillovers in Networks of User Generated Content: Pseudo-Experimental Evidence on Wikipedia

Abstract

I quantify spillovers of attention in a network of content pages, which is challenging, because such networks form endogenously. I exploit exogenous variation in the article network of German Wikipedia to circumvent this problem. Wikipedia prominently advertises one featured article on its main site every day, which increases viewership of the advertised article. Shifts in the viewership of adjacent articles are due to their link from the treated article. Through this approach I isolate how the link network causally influences users’ search and contribution behavior. I use a difference-in-differences analysis to estimate how attention spills to neighbors through the transient shock of advertisement. I further develop an extended peer effects model which relaxes the requirement of an exogenously given network. This model enables the estimation of the underlying spillover. Advertisements affect neighboring articles substantially: Their viewership increases by almost 70 percent. This, in turn, translates to increased editing activity. Attention is the driving mechanism behind views and short edits. Both outcomes are related to the order of links, while more substantial edits are not.