Analysis of keyword‐based tagging behaviors of experts and novices

Abstract

PurposeExpert and novice readers tag documents with different descriptions; this study is intended to discover which readers would generate the most reliable and most representative sets of tags.Design/methodology/approachOne group of experts and one group of novices were recruited. These two groups were asked to provide tags for document bookmarks in a Mozilla Firefox browser. In the experimental analysis we defined two measures – similarity and relevance – to describe the differences between the two groups.FindingsTags chosen by experts yielded better similarity and relevance values in all analyses. Tags chosen by the expert group had higher commonality in pairwise similarity analysis; moreover, the relevance analysis showed that tags chosen by experts reflected better understanding of the content.Originality/valueTagging behavior has become highly popular on the web, and its study has commercial merit. Tags from experts represent the structure behind the knowledge involved; expert representation may be vastly more helpful than novice representation for promoting understanding of content in an era characterized by an explosion of information.

Time‐based tags for fiction movies: comparing experts to novices using a video labeling game

“…In our study, we focus on the relation between the types of generated tags and the participants' knowledge of the domain. Tsai, Hwang, and Tang () looked at whether experts can provide a more consistent and representative set of tags for academic and scientific documents than novices, in the context of nanomaterial technology. They concluded that tags chosen by experts yielded better similarity and relevance values in all analyses and that these tags reflected better understanding of the content.…”

A Tag‐Based Approach for Learning Ergonomic Concepts

“…Current tag extraction does not always produce tags that represent abstract concepts well. Better domain knowledge could extract better tags from web documents (Tsai, Hwang, & Tang, ) or lead to improved searching performance with better query terms (Vibert et al., ), especially in so‐called hard content, that is, scientific articles or professional academic articles (Tsai, Hwang, & Tang, ). Studies of expertise have demonstrated (e.g., see Held, 2009) that experts retain a large body of domain knowledge, and there exists a particularly efficient structure with strong connections among chunks of data that can be retrieved without much effort.…”

“…A participant's tag was defined to be “similar” if it matched the experts’ tags. The semantic relatedness between every set of new tags created and the existing expert tags was estimated with Jaccard's similarity coefficient equation, a statistic used for comparing the similarity and diversity of sample sets, defined between sets A and B (Hooper, ; Tsai, Hwang, & Tang, ). For example, the overlap between the tag set {human error, alarm‐displays, frequency, threshold, signal detection theory} and the tag set {signal detection theory, overload, masking, frequency, alarm‐displays} consisted of the three tags {alarm‐displays, signal detection theory, frequency}, and the tag similarity between this pair of tag sets was 3/(5 + 5−3) = 0.43 for this article.…”

“…A prior study (Tsai, Hwang, & Tang, ) suggested that, compared to novices, experts are more proficient at extracting concepts and key components from a hard‐content article and create tags of higher quality. In the social‐tagging context, we believe that high‐quality tags generated by the consensus of a group of experts may facilitate an exploratory self‐learning process and domain knowledge understanding for novice learners.…”

The development and validation of a one‐bit comparison for evaluating the maturity of tag distributions in a Web 2.0 environment

“…Tags describing informal information, or “soft content,” which involve personal experiences, such as blogs, are typically intrinsic to users. However, tags describing formal information, or “hard content,” which includes professional or academic materials, are typically extrinsic to users and are related to the theme of the content (Tsai, Hwang, & Tang, ).…”