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

Authors:
Wai-Tat Fu;Vera Liao
Affiliations:
Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL;Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL
Venue:
SBP'11 Proceedings of the 4th international conference on Social computing, behavioral-cultural modeling and prediction
Year:
2011

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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.