A study on mental models of taggers and experts for article indexing based on analysis of keyword usage

This article explores the mental models of article indexing of taggers and experts in keyword usage. Better understanding of the mental models of taggers and experts and their usage gap may inspire better selection of appropriate keywords for organizing information resources. Using a data set of 3,972 tags from CiteULike and 6,708 descriptors from Library and Information Science Abstracts (LISA) from 1,489 scholarly articles of 13 library and information science journals, social network analysis and frequent-pattern tree methods were used to capture and build up the mental models of article indexing of taggers and experts when using keywords, and to generalize their structures and patterns. When measured with respect to the terms used, a powerlaw distribution, a comparison of terms used as tags and descriptors, social network analysis (including centrality, overall structure, and role equivalent) and frequentpattern tree analysis, little similarity was found between the mental models of taggers and experts. Twenty-five patterns of path-based rules and 12 identical rules of frequent-pattern trees were shared by taggers and experts. Title-and topic-related keyword categories were the most popular keyword categories used in pathbased rules of frequent-pattern trees, and also the most popular members of 25 patterns and the starting point of the 12 identical rules.