Predicting the Usefulness of Questions in Q&A Communities: A Comparison of Classical Machine Learning and Deep Learning Approaches

Abstract

Questioning and answering (Q&A) communities have become an important platform for online knowledge exchange. With a vast number of questions posted to elicit high-quality solutions as well as a large number of participants engaged in online knowledge sharing, a grand challenge for Q&A communities is thus to effectively and efficiently identify and rank useful questions. The current approach to solving this problem is either through user voting or by community moderators. However, such manual processes are limited in terms of efficiency and scalability, especially for large Q&A communities. Thus, automatically predicting the usefulness of questions has significant implications for the management of online Q&A communities. To provide guidelines for assessing the quality of online questions, this research investigates and compares various classical machine learning and deep learning methods for predicting question usefulness. A dataset collected from a large Q&A community was used to train and test those machine learning methods. The findings of this research provide important implications for both the research and practice of online Q&A communities.

Department(s)

Business and Information Technology

Keywords and Phrases

Deep Learning; Machine Learning; Q&A Communities; Question Usefulness

International Standard Book Number (ISBN)

978-303105543-0

International Standard Serial Number (ISSN)

1611-3349; 0302-9743

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2022 Springer, All rights reserved.

Publication Date

01 Jan 2022

Link to Full Text

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