Crowdsourcing quality control of online information: A quality-based cascade model

Wai Tat Fu, Vera Liao

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

Abstract

We extend previous cascade models of social influence to investigate how the exchange of quality information among users may moderate cascade behavior, and the extent to which it may influence the effectiveness of collective user recommendations on quality control of information. We found that while cascades do sometimes occur, their effects depend critically on the accuracies of individual quality assessments of information contents. Contrary to predictions of cascade models of information flow, quality-based cascades tend to reinforce the propagation of individual quality assessments rather than being the primary sources that drive the assessments. We found even small increase in individual accuracies will significantly improve the overall effectiveness of crowdsourcing quality control. Implication to domains such as online health information Web sites or product reviews are discussed.

Original languageEnglish (US)
Title of host publicationSocial Computing, Behavioral-Cultural Modeling and Prediction - 4th International Conference, SBP 2011, Proceedings
Pages147-154
Number of pages8
DOIs
StatePublished - 2011
Externally publishedYes
Event4th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2011 - College Park, MD, United States
Duration: Mar 29 2011Mar 31 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6589 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2011
Country/TerritoryUnited States
CityCollege Park, MD
Period3/29/113/31/11

Keywords

  • Web quality
  • information cascades
  • social dynamics
  • user reviews

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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