Measure and mitigate the dimensional bias in online reviews and ratings

Yong Ge, Jingjing Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

Online word-of-mouth in the form of online reviews and ratings is an increasingly important resource for consumers to acquire product information for their purchase decision. However, dimensional review bias, originated from consumer heterogeneity and their multidimensional product preferences and experiences, have been shown to undermine the information transfer among consumers. Through a novel text mining approach, we identify and quantify two types of dimensional biases from textual reviews: Dimensional preference bias and dimensional rating bias. We also introduce a quantitative method to mitigate the dimensional rating bias. We examined the effectiveness and applicability of our bias measures and de-bias method in the context of multi-dimensional and single-dimensional rating systems. Specifically, we focused on the hotel reviews from TripAdvisor.com and Expedia.com. Our preliminary results show promising theoretical and managerial contributions.

Original languageEnglish (US)
Title of host publication2015 International Conference on Information Systems
Subtitle of host publicationExploring the Information Frontier, ICIS 2015
PublisherAssociation for Information Systems
ISBN (Print)9780996683111
StatePublished - 2015
Externally publishedYes
Event2015 International Conference on Information Systems: Exploring the Information Frontier, ICIS 2015 - Fort Worth, United States
Duration: Dec 13 2015Dec 16 2015

Publication series

Name2015 International Conference on Information Systems: Exploring the Information Frontier, ICIS 2015

Other

Other2015 International Conference on Information Systems: Exploring the Information Frontier, ICIS 2015
Country/TerritoryUnited States
CityFort Worth
Period12/13/1512/16/15

Keywords

  • Dimensional bias
  • Online review
  • Rating bias
  • Review bias

ASJC Scopus subject areas

  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences
  • Applied Mathematics

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