Community Clustering: Leveraging an Academic Crowd to Form Coherent Conference Sessions


  • Paul André Carnegie Mellon University
  • Haoqi Zhang Northwestern University
  • Juho Kim Massachusetts Institute of Technology
  • Lydia Chilton University of Washington
  • Steven Dow Carnegie Mellon University
  • Robert Miller Massachusetts Institute of Technology



Creating sessions of related papers for a large conference is a complex and time-consuming task. Traditionally, a few conference organizers group papers into sessions manually. Organizers often fail to capture the affinities between papers beyond created sessions, making incoherent sessions difficult to fix and alternative groupings hard to discover. This paper proposes committeesourcing and authorsourcing approaches to session creation (a specific instance of clustering and constraint satisfaction) that tap into the expertise and interest of committee members and authors for identifying paper affinities. During the planning of ACM CHI'13, a large conference on human-computer interaction, we recruited committee members to group papers using two online distributed clustering methods. To refine these paper affinities — and to evaluate the committeesourcing methods against existing manual and automated approaches — we recruited authors to identify papers that fit well in a session with their own. Results show that authors found papers grouped by the distributed clustering methods to be as relevant as, or more relevant than, papers suggested through the existing in-person meeting. Results also demonstrate that communitysourced results capture affinities beyond sessions and provide flexibility during scheduling.




How to Cite

André, P., Zhang, H., Kim, J., Chilton, L., Dow, S., & Miller, R. (2013). Community Clustering: Leveraging an Academic Crowd to Form Coherent Conference Sessions. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 1(1), 9-16.