Specialization, homophily, and gender in a social curation site: Findings from pinterest

Shuo Chang, Vikas Kumar, Eric Gilbert, Loren Terveen

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

62 Scopus citations

Abstract

Pinterest is a popular social curation site where people collect, organize, and share pictures of items. We studied a fundamental issue for such sites: what patterns of activity attract attention (audience and content reposting)? We organized our studies around two key factors: the extent to which users specialize in particular topics, and homophily among users. We also considered the existence of differences between female and male users. We found: (a) women and men differed in the types of content they collected and the degree to which they specialized; male Pinterest users were not particularly interested in stereotypically male topics; (b) sharing diverse types of content increases your following, but only up to a certain point; (c) homophily drives repinning: people repin content from other users who share their interests; homophily also affects following, but to a lesser extent. Our findings suggest strategies both for users (e.g., strategies to attract an audience) and maintainers (e.g., content recommendation methods) of social curation sites.

Original languageEnglish (US)
Title of host publicationCSCW 2014 - Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work and Social Computing
PublisherAssociation for Computing Machinery
Pages674-686
Number of pages13
ISBN (Print)9781450325400
DOIs
StatePublished - Jan 1 2014
Event17th ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW 2014 - Baltimore, MD, United States
Duration: Feb 15 2014Feb 19 2014

Publication series

NameProceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW

Other

Other17th ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW 2014
CountryUnited States
CityBaltimore, MD
Period2/15/142/19/14

Keywords

  • Data analysis
  • Social network
  • Topic detection
  • User profiling

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