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.
ASWEC 2015 : Proceedings of the 24th Australasian Software Engineering Conference
Place of publication
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).
Field of Research
080109 Pattern Recognition and Data Mining
Socio Economic Objective
970108 Expanding Knowledge in the Information and Computing Sciences
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