Finding subject terms for classificatory metadata from user‐generated social tags

Abstract

With the increasing popularity of social tagging systems, the potential for using social tags as a source of metadata is being explored. Social tagging systems can simplify the involvement of a large number of users and improve the metadata-generation process. Current research is exploring social tagging systems as a mechanism to allow nonprofessional catalogers to participate in metadata generation. Because social tags are not from controlled vocabularies, there are issues that have to be addressed in finding quality terms to represent the content of a resource. This research explores ways to obtain a set of tags representing the resource from the tags provided by users. Two metrics are introduced. Annotation Dominance (AD) is a measure of the extent to which a tag term is agreed to by users. Cross Resources Annotation Discrimination (CRAD) is a measure of a tag's potential to classify a collection. It is designed to remove tags that are used too broadly or narrowly. Using the proposed measurements, the research selects important tags (meta-terms) and removes meaningless ones (tag noise) from the tags provided by users. To evaluate the proposed approach to find classificatory metadata candidates, we rely on expert users' relevance judgments comparing suggested tag terms and expert metadata terms. The results suggest that processing of user tags using the two measurements successfully identifies the terms that represent the topic categories of web resource content. The suggested tag terms can be further examined in various usages as semantic metadata for the resources.

Enhancing social tagging with automated keywords from the Dewey Decimal Classification

“…Weller (2007) compared ontologies and folksonomies, suggesting that they are not to be seen as rivals but complementary to each other. Syn and Spring (2013) show that social tags can be used as metadata and present metrics to select the tags which have that potential. Noruzi (2007) gave seven arguments for why a folksonomy-based system should use a thesaurus, emphasizing that it is impossible to maintain consistency over time or across folksonomy users without a thesaurus.…”

Ontology for cultural variations in interpersonal communication: Building on theoretical models and crowdsourced knowledge

“…Computational models and tools are emerging in the fields of data mining and semantic web to assist people to make sense of UGC (Leginus, Zhai, & Dolog, ; Syn & Spring, ). Data mining theories and tools (Fayyad, Piatetsky‐Shapiro, & Smyth ) exploit the volumes of data to derive interesting patterns; for instance, sentiment analysis (Nasukawa & Yi, ), also called opinion mining, is used to analyze people's views towards entities such as products, organizations, or news events.…”

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

“…Studies indicated that despite a lack of controlled vocabulary to constrain the actions of individual users, the distribution of tags converges over time to a stable power law distribution (e.g., Robu, Halpin, & Shepherd, ), revealing that the majority of users agree on several widely used tags, and that only small groups prefer obscure terms to describe items of interest (Syn & Spring, ). This phenomenon emerges from an inherent imitation behavior called social influence.…”