The impact of social information on visual judgments

Jessica Hullman*, Eytan Adar, Priti Shah

*Corresponding author for this work

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

49 Scopus citations


Social visualization systems have emerged to support collective intelligence-driven analysis of a growing influx of open data. As with many other online systems, social signals (e.g., forums, polls) are commonly integrated to drive use. Unfortunately, the same social features that can provide rapid, high-accuracy analysis are coupled with the pitfalls of any social system. Through an experiment involving over 300 subjects, we address how social information signals (social proof) affect quantitative judgments in the context of graphical perception. We identify how unbiased social signals lead to fewer errors over non-social settings and conversely, how biased signals lead to more errors. We further reflect on how systematic bias nullifies certain collective intelligence benefits, and we provide evidence of the formation of information cascades. We describe how these findings can be applied to collaborative visualization systems to produce more accurate individual interpretations in social contexts.

Original languageEnglish (US)
Title of host publicationCHI 2011 - 29th Annual CHI Conference on Human Factors in Computing Systems, Conference Proceedings and Extended Abstracts
Number of pages10
StatePublished - 2011
Event29th Annual CHI Conference on Human Factors in Computing Systems, CHI 2011 - Vancouver, BC, Canada
Duration: May 7 2011May 12 2011

Publication series

NameConference on Human Factors in Computing Systems - Proceedings


Other29th Annual CHI Conference on Human Factors in Computing Systems, CHI 2011
CityVancouver, BC


  • Graphical perception
  • Information visualization
  • Mechanical turk
  • Social influence
  • Social proof

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design


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