Content growth and attention contagion in information networks: Addressing information poverty on Wikipedia

Kai Zhu, Dylan Walker, Lev Muchnik

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Open collaboration platforms have fundamentally changed the way that knowledge is produced, disseminated, and consumed. In these systems, contributions arise organically with little to no central governance. Although such decentralization provides many benefits, a lack of broad oversight and coordination can leave questions of information poverty and skewness to the mercy of the system’s natural dynamics. Unfortunately, we still lack a basic understanding of the dynamics at play in these systems and specifically, how contribution and attention interact and propagate through information networks. We leverage a large-scale natural experiment to study how exogenous content contributions to Wikipedia articles affect the attention that they attract and how that attention spills over to other articles in the network. Results reveal that exogenously added content leads to significant, substantial, and long-term increases in both content consumption and subsequent contributions. Furthermore, we find significant attention spillover to downstream hyperlinked articles. Through both analytical estimation and empirically informed simulation, we evaluate policies to harness this attention contagion to address the problem of information poverty and skewness. We find that harnessing attention contagion can lead to as much as a twofold increase in the total attention flow to clusters of disadvantaged articles. Our findings have important policy implications for open collaboration platforms and information networks.

Original languageAmerican English
Pages (from-to)491-509
Number of pages19
JournalInformation Systems Research
Volume31
Issue number2
DOIs
StatePublished - Jun 2020

Bibliographical note

Publisher Copyright:
Copyright: © 2020 INFORMS.

Keywords

  • Attention contagion
  • Information consumption
  • Open collaboration platform
  • Spillover effect
  • User-generated content

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