"Popularity effect" in user-generated content: Evidence from online product reviews

Paulo B. Goes, Mingfeng Lin, Ching man Au Yeung

Research output: Contribution to journalArticle

146 Scopus citations

Abstract

Online product reviews are increasingly important for consumer decisions, yet we still know little about how reviews are generated in the first place. In an effort to gather more reviews, many websites encourage user interactions such as allowing one user to subscribe to another. Do these interactions actually facilitate the generation of product reviews? More importantly, what kind of reviews do such interactions induce? We study these questions using data from one of the largest product review websites where users can subscribe to one another. By applying both panel data and a flexible matching method, we find that as users become more popular, they produce more reviews and more objective reviews; however, their numeric ratings also systematically change and become more negative and more varied. Such trade-off has not been previously documented and has important implications for both product review and other user-generated content websites.

Original languageEnglish (US)
Pages (from-to)222-238
Number of pages17
JournalInformation Systems Research
Volume25
Issue number2
DOIs
StatePublished - Jun 2014

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Keywords

  • Matching
  • Online community
  • Opinion leader
  • Popularity
  • Product reviews
  • Social media
  • Text mining
  • User-generated content

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Computer Networks and Communications
  • Information Systems and Management
  • Library and Information Sciences

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