Early detection of potential experts in question answering communities

By Aditya Pal, Rosta Farzan, J. Konstan, Robert E. Kraut. 2011

102 citations recorded by the source corpus.

Question answering communities (QA) are sustained by a handful of experts who provide a large number of high quality answers. Identifying these experts during the first few weeks of their joining the community can be beneficial as it would allow community managers to take steps to develop and retain these potential experts. In this paper, we explore approaches to identify potential experts as early as within the first two weeks of their association with the QA. We look at users' behavior and estimate their motivation and ability to help others. These qualities enable us to build classification and ranking models to identify users who are likely to become experts in the future. Our results indicate that the current experts can be effectively identified from their early behavior. We asked community managers to evaluate the potential experts identified by our algorithm and their analysis revealed that quite a few of these users were already experts or on the path of becoming experts. Our retrospective analysis shows that some of these potential experts had already left the community, highlighting the value of early identification and engagement.

Subfields: Artificial Intelligence, Computer Science Applications, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Julian McAuley
2. Jure Leskovec
3. Darren Gergle
4. Yongfeng Zhang
5. Xu Chen
6. Xiangnan He
7. Philip S. Yu
8. William L. Hamilton

In Computer Science Applications

1. Jure Leskovec
2. Darren Gergle
3. Julian McAuley

In Information Systems

1. Julian McAuley
2. Jure Leskovec
3. Darren Gergle
4. Yongfeng Zhang
5. Xiangnan He
6. Philip S. Yu
7. Ruining He
8. Xu Chen

Citations in the corpus

• From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews (2013, background): “We also briefly mention the topic of expertise identification [1, 6, 16, 32].”
• IntroAssist: A Tool to Support Writing Introductory Help Requests (2018, background): “Even if an algorithm recommends an ideal match based on target expertise, willingness to help, or similar interests [8,31,34,38,40,43,50,69], people reaching out might still fail to send a help request that resonates with the receiver enough for them to respond.”