research-article

Min(e)d your tags: analysis of question response time in stackoverflow

Authors:

Adheesh Gokhale,

Jagat Pudipeddi,

Leman AkogluAuthors Info & Claims

ASONAM '14: Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Pages 328 - 335

Published: 17 August 2014 Publication History

Abstract

Given a newly posted question on a Question and Answer (Q&A) site, how long will it take until an answer is received? Does response time relate to factors about how the question asker composes their question? If so, what are those factors? With advances in social media and the Web, Q&A sites have become a major source of information for Internet users. Response time of a question is an important aspect in these sites as it is associated with the users' satisfaction and engagement, and thus the lifespan of these online communities. In this paper we study and estimate response time for questions in StackOverflow, a popular online Q&A forum where software developers post and answer questions related to programming. We analyze a long list of factors in the data and identify those that have clear relation with response time. Our key finding is that tag-related factors, such as their "popularity" (how often the tag is used) and the number of their "subscribers" (how many users can answer questions containing the tag), provide much stronger evidence than factors not related to tags. Finally, we learn models using the identified evidential features for predicting the response time of questions, which also demonstrate the significance of tags chosen by the question asker.

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Cited By

Dietrich JLuczak-Roesch MDalefield EStorey MAdams BHaiduc S(2019)Man vs machineProceedings of the 16th International Conference on Mining Software Repositories10.1109/MSR.2019.00041(205-209)Online publication date: 26-May-2019
https://dl.acm.org/doi/10.1109/MSR.2019.00041
Li ZYin GWang TZhang YYu YWang H(2018)Correlation-based software search by leveraging software term databaseFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-017-6573-z12:5(923-938)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1007/s11704-017-6573-z

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Published In

cover image ACM Conferences

ASONAM '14: Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

August 2014

1021 pages

ISBN:9781479958764

Conference Chairs:
Yan Jia
National University of Defence Technology, China
,
Jon Rokne
University of Calgary, Canada
,
Program Chairs:
Xindong Wu
University of Vermont and Hefei University of Technology, China
,
Martin Ester
Simon Fraser University, Canada
,
Guandong Xu
University of Technology Sydney, Australia

Sponsors

SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
IEEE-CS\DATC: IEEE Computer Society

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IEEE Press

Publication History

Published: 17 August 2014

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ASONAM '14

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SIGKDD
IEEE-CS\DATC

ASONAM '14: Advances in Social Networks Analysis and Mining 2014

August 17 - 20, 2014

Beijing, China

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Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

Dietrich JLuczak-Roesch MDalefield EStorey MAdams BHaiduc S(2019)Man vs machineProceedings of the 16th International Conference on Mining Software Repositories10.1109/MSR.2019.00041(205-209)Online publication date: 26-May-2019
https://dl.acm.org/doi/10.1109/MSR.2019.00041
Li ZYin GWang TZhang YYu YWang H(2018)Correlation-based software search by leveraging software term databaseFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-017-6573-z12:5(923-938)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1007/s11704-017-6573-z

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