Comparing social tags with subject headings on annotating books: A study comparing the information science domain in English and Chinese

Abstract

The literature often views the emergence of social tagging as a potential alternative method to controlled vocabulary for organizing and indexing large-scale information resources. In this paper, we present an in-depth examination of the relationship between social tagging and controlled vocabulary-based indexing and organization in two unique contexts: the information science domain and when comparing data gathered from both English and Chinese sources. Our results show that the information science domain has more overlap between social tags and controlled vocabulary-based subject terms. This is reflected in the higher percentage of overlapping terms between tags and subject terms, as well as in the strong similarity (measured by Jaccard’s coefficient) in frequently used keywords among tags and subject terms. However, social tags in the information science domain still possess limitations in terms of uncontrolled terms, where inconsistencies and noisy usages exist. Our results also show that language difference does have an impact on social tagging. The numbers of Chinese tags overall and per book are less than those of English tags. The most frequently used English tags are single-word terms, which are different from multi-word controlled vocabulary terms. In comparison, the character difference between the most frequently used Chinese tags and Chinese subject terms is just one character (3 vs 4). However, English and Chinese users do share many similar behaviours when they tag books in the information science domain. Many of the most frequently used tags are shared between the two languages and the patterns of overlap between topical tags and subject terms are also similar between the two languages. Overall, despite the application limitations for social tagging in cataloguing and indexing, we believe that tagging has the potential to become a complementary resource for expanding and enriching controlled vocabulary systems. With the help of future technology to regulate and promote features related to controlled vocabulary in social tags, a hybrid cataloguing and indexing system that integrates social tags with controlled vocabulary would greatly improve people’s organizational and access capabilities within information resources.

The impact of image descriptions on user tagging behavior: A study of the nature and functionality of crowdsourced tags

“…Crowdsourced tags are also called "social tags" or "collaborative annotations" in the literature. They are outcomes of a distributed practice performed by Internet users in organizing and indexing online digital objects, such as web pages, video clips, and images (Wu, He, Qiu, Lin, & Liu, 2012). Because they fit well with the social web's general principle of sharing and participating, crowdsourced tags quickly established themselves as one of the major forces for converting the static Web into a participatory information space (Ding, Jacob, Yan, George, & Guo, 2009).…”

Tagging in Volunteered Geographic Information: An Analysis of Tagging Practices for Cities and Urban Regions in OpenStreetMap

“…Total number of objects: 470 sport 364 77.5% GOOD surface 42 9.0% POOR Total number of different tags used: 26 …”

“…There are a number of possible pathways to further investigation for this problem. In [42], the authors considered comparing social tags and subject terms in the domain of information science between Chinese and English sources. The authors used traditional methods such as the Jaccard similarity coefficient and the Spearman correlation coefficients of the two ranked sets to compare these tag sets.…”

How users employ various popular tags to annotate resources in social tagging: An empirical study

“…Second, the correspondence or association between tags and other information resources has been analyzed. In this case, quantitative examination of the overlaps or similarities between social tags and other information resources is conducted based on text extraction and text matching, such as the similarities between tags and titles and between tags and texts (Arolas & Ladrón‐de‐Guevar, ; Heymann, Koutrika, & Garcia‐Molina, ), the overlaps between tags and web queries (Yi & Yoo, ), and between tags and controlled vocabulary‐based subject terms (Wu, He, Qiu, Lin, & Liu, ).…”