Accounting for taste: Ranking curators and content in social networks

Haizi Yu, Biplab Deka, Jerry O. Talton, Ranjitha Kumar

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


Ranking users in social networks is a well-studied problem, typically solved by algorithms that leverage network structure to identify influential users and recommend people to follow. In the last decade, however, curation - users sharing and promoting content in a network - has become a central social activity, as platforms like Facebook, Twitter, Pinterest, and GitHub drive growth and engagement by connecting users through content and content to users. While existing algorithms reward users that are highly active with higher rankings, they fail to account for users' curatorial taste. This paper introduces CuRank, an algorithm for ranking users and content in social networks by explicitly modeling three characteristics of a good curator: discerning taste, high activity, and timeliness. We evaluate CuRank on datasets from two popular social networks - GitHub and Vine - and demonstrate its efficacy at ranking content and identifying good curators.

Original languageEnglish (US)
Title of host publicationCHI 2016 - Proceedings, 34th Annual CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery
Number of pages7
ISBN (Electronic)9781450333627
StatePublished - May 7 2016
Event34th Annual Conference on Human Factors in Computing Systems, CHI 2016 - San Jose, United States
Duration: May 7 2016May 12 2016

Publication series

NameConference on Human Factors in Computing Systems - Proceedings


Other34th Annual Conference on Human Factors in Computing Systems, CHI 2016
Country/TerritoryUnited States
CitySan Jose


  • Content
  • Curation
  • Ranking
  • Social networks

ASJC Scopus subject areas

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

Cite this