It takes two to tango: Deleted Stack Overflow question prediction with text and meta features

Publication Type

Conference Proceeding Article

Publication Date

6-2016

Abstract

Stack Overflow is a popular community-based Q&A website that caters to technical needs of software developers. As of February 2015 - Stack Overflow has more than 3.9M registered users, 8.8M questions, and 41M comments. Stack Overflow provides explicit and detailed guidelines on how to post questions but, some questions are very poor in quality. Such questions are deleted by the experienced community members and moderators. Deleted questions increase maintenance cost and have an adverse impact on the user experience. Therefore, predicting deleted questions is an important task. In this study, we propose a two stage hybrid approach - DelPredictor - which combines text processing and classification techniques to predict deleted questions. In the first stage, DelPredictor converts text in the title, body, and tag fields of questions into numerical textual features via text processing and classification techniques. In the second stage, it extracts meta features that can be categorized into: profile, community, content, and syntactic features. Next, it learns and combines two independent classifiers built on the textual and meta features. We evaluate DelPredictor on 5 years (2008 - 2013) of deleted questions from Stack Overflow. Our experimental results show that DelPredictor improves the F1-scores over baseline prediction, a prior approach [12] and a text-based approach by 29.50%, 9.34%, and 28.11%, respectively.

Keywords

Classification, Deleted Question, Stack Overflow, Text Processing

Discipline

Computer Sciences | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

COMPSAC 2016: Proceedings of the 40th IEEE Annual International Computers, Software and Applications Conference: Atlanta, Georgia, 10-14 June 2016

First Page

73

Last Page

82

ISBN

9781467388450

Identifier

10.1109/COMPSAC.2016.145

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

Additional URL

http://doi.org/10.1109/COMPSAC.2016.145

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