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2021, Volume 22, Issue 2

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Frontiers of Information Technology & Electronic Engineering >> 2021, Volume 22, Issue 2 doi: 10.1631/FITEE.1900645

Learning natural ordering of tags in domain-specific Q&A sites

Affiliation(s): School of Computer and Network Engineering, Shanxi Datong University, Datong 037009, China; School of Software, Shanghai Jiao Tong University, Shanghai 200240, China; less

Received: 2019-11-24 Accepted: 2021-02-01 Available online: 2021-02-01
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Abstract

is a defining characteristic of Web 2.0. It allows users of social computing systems (e.g., ) to use free terms to annotate content. However, is really a free action? Existing work has shown that users can develop implicit consensus about what tags best describe the content in an online community. However, there has been no work studying the regularities in how users order tags during . In this paper, we focus on the ing of tags in domain-specific Q&A sites. We study tag sequences of millions of questions in four Q&A sites, i.e., CodeProject, SegmentFault, Biostars, and CareerCup. Our results show that users of these Q&A sites can develop implicit consensus about in which order they should assign tags to questions. We study the relationships between tags that can explain the emergence of ing of tags. Our study opens the path to improve existing tag recommendation and Q&A site navigation by leveraging the ing of tags.

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