Who will answer my question on Stack Overflow?

Choetkiertikul, Morakot, Avery, Daniel, Dam, Hoa Khanh, Tran, Truyen and Ghose, Aditya 2015, Who will answer my question on Stack Overflow?, in ASWEC 2015 : Proceedings of the 24th Australasian Software Engineering Conference, IEEE, Piscataway, N.J., pp. 155-164, doi: 10.1109/ASWEC.2015.28.

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Title Who will answer my question on Stack Overflow?
Author(s) Choetkiertikul, Morakot
Avery, Daniel
Dam, Hoa Khanh
Tran, TruyenORCID iD for Tran, Truyen orcid.org/0000-0001-6531-8907
Ghose, Aditya
Conference name Australasian Software Engineering. Conference (24th : 2015 : Adelaide, South Australia)
Conference location Adelaide, South Australia
Conference dates 28 Sept. - 1 Oct. 2015
Title of proceedings ASWEC 2015 : Proceedings of the 24th Australasian Software Engineering Conference
Publication date 2015
Start page 155
End page 164
Total pages 10
Publisher IEEE
Place of publication Piscataway, N.J.
Summary Stack Overflow is a highly successful Community Question Answering (CQA) service for software developers with more than three millions users and more than ten thousand posts per day. The large volume of questions makes it difficult for users to find questions that they are interested in answering. In this paper, we propose a number of approaches to predict who will answer a new question using the characteristics of the question (i.e. Topic) and users (i.e. Reputation), and the social network of Stack Overflow users (i.e. Interested in the same topic). Specifically, our approach aims to identify a group of users (candidates) who have the potential to answer a new question by using feature-based prediction approach and social network based prediction approach. We develop predictive models to predict whether an identified candidate answers a new question. This prediction helps motivate the knowledge exchanging in the community by routing relevant questions to potential answerers. The evaluation results demonstrate the effectiveness of our predictive models, achieving 44% precision, 59% recall, and 49% F-measure (average across all test sets). In addition, our candidate identification techniques can identify the answerers who actually answer questions up to 12.8% (average across all test sets).
ISSN 1530-0803
Language eng
DOI 10.1109/ASWEC.2015.28
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30081483

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