International Conference on User Modeling, Adaptation, and Personalization

UMAP 2015: User Modeling, Adaptation and Personalization pp 315-327 | Cite as

Harnessing Engagement for Knowledge Creation Acceleration in Collaborative Q&A Systems

  • Jie YangEmail author
  • Alessandro BozzonEmail author
  • Geert-Jan HoubenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9146)


Thanks to reputation and gamification mechanisms, collaborative question answering systems coordinate the process of topical knowledge creation of thousands of users. While successful, these systems face many challenges: on one hand, the volume of submitted questions overgrows the amount of new users willing, and capable, of answering them. On the other hand, existing users need to be retained and optimally allocated. Previous work demonstrates the positive effects that two important aspects, namely engagement and expertise valorisation, can have on user quality and quantity of participation. The magnitude of their effect can greatly vary across users and across topics. In this paper we advocate for a more in-depth study of the interplay that exists between user engagement factors in question answering systems. Our working hypothesis is that the process of knowledge creation can be accelerated by better understanding and exploiting the combined effects of the interests and expertise of users, with their intrinsic and extrinsic motivations. We perform a study over 6 years of data from the StackOverflow platform. By defining metrics of expertise and (intrinsic and extrinsic) motivations, we show how they distribute and correlate across platform’s users and topics. By means of an off-line question routing experiment, we show how topic-specific combinations of motivations and expertise can help accelerating the knowledge creation process.


Intrinsic Motivation Knowledge Creation Extrinsic Motivation Question Answering User Engagement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Delft University of TechnologyDelftThe Netherlands

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