What Do User Experience Professionals Discuss Online? Topic Modeling of a User Experience Q&A Community
Abstract
Questioning and Answering (Q&A) Communities Have Been Widely Used by User Experience (UX) Professionals to Exchange Knowledge Online. as Online Content Has Been Increasingly Generated by Professional Users in the Community, It Becomes Infeasible for Human Experts to Manually Analyze Those Discussions. Thus, Automatic and Intelligent Analysis of the Online Content Generated by UX Professionals is Needed to Understand How UX Knowledge is Created and Maintained by Professionals in Online Communities. This Research Offers a Comprehensive Understanding of User-Generated Content in a UX Q&A Community through Topic Modeling, an Intelligent Text-Mining Approach for Discovering Hidden Topics from Textual Documents. Specifically, This Research Identifies 40 Important Latent Discussion Topics from a Dataset Containing 31,314 Questions and 80,579 Answers Posted by UX Professionals. Those Topics Are Classified into 8 Major Categories, Followed by Popularity and Content Cohesion Analysis of Those Categories and their Mapping to a Well-Established Design Thinking Process Model. overall, This Research Contributes a Systematic Exploration of User-Generated Content by UX Professionals in a Q&A Community and Highlights Opportunities for UX Researchers and Practitioners.
Recommended Citation
Chen, L. (2023). What Do User Experience Professionals Discuss Online? Topic Modeling of a User Experience Q&A Community. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14038 LNCS, pp. 365-380. Springer Nature Switzerland AG.
The definitive version is available at https://doi.org/10.1007/978-3-031-35969-9_25
Department(s)
Business and Information Technology
Keywords and Phrases
Latent Dirichlet Allocation; Q&A Communities; Topic Modeling; User Experience (UX); User-Generated Content
International Standard Book Number (ISBN)
978-303135968-2
International Standard Serial Number (ISSN)
1611-3349; 0302-9743
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2023 Springer Nature Switzerland AG, All rights reserved.
Publication Date
01 Jan 2023