Usefulness of social tagging in organizing and providing access to the web: An analysis of indexing consistency and quality

This dissertation research points out major challenging problems with current Knowledge Organization (KO) systems, such as subject gateways or web directories: (1) the current systems use traditional knowledge organization systems based on controlled vocabulary which is not very well suited to web resources, and (2) information is organized by professionals not by users, which means it does not reflect intuitively and instantaneously expressed users’ current needs. In order to explore users’ needs, I examined social tags which are user-generated uncontrolled vocabulary. As investment in professionally-developed subject gateways and web directories diminishes (support for both BUBL and Intute, examined in this study, is being discontinued), understanding characteristics of social tagging becomes even more critical. Several researchers have discussed social tagging behavior and its usefulness for classification or retrieval; however, further research is needed to qualitatively and quantitatively investigate social tagging in order to verify its quality and benefit. This research particularly examined the indexing consistency of social tagging in comparison to professional indexing to examine the quality and efficacy of tagging. The data analysis was divided into three phases: analysis of indexing consistency, analysis of tagging effectiveness, and analysis of tag attributes. Most indexing consistency studies have been conducted with a small number of professional indexers, and they tended to exclude users. Furthermore, the studies mainly have focused on physical library collections. This dissertation research bridged these gaps by (1) extending the scope of resources to various web documents indexed by users and (2) employing the Information Retrieval (IR) Vector Space Model (VSM) - based indexing consistency method since it is suitable for dealing with a large number of indexers. As a second phase, an analysis of tagging effectiveness with tagging exhaustivity and tag specificity was conducted to ameliorate the drawbacks of consistency analysis based on only the quantitative measures of vocabulary matching. Finally, to investigate tagging pattern and behaviors, a content analysis on tag attributes was conducted based on the FRBR model. The findings revealed that there was greater consistency over all subjects among taggers compared to that for two groups of professionals. The analysis of tagging exhaustivity and tag specificity in relation to tagging effectiveness was conducted to ameliorate difficulties associated with limitations in the analysis of indexing consistency based on only the quantitative measures of vocabulary matching. Examination of exhaustivity and specificity of social tags provided insights into particular characteristics of tagging behavior and its variation across subjects. To further investigate the quality of tags, a Latent Semantic Analysis (LSA) was conducted to determine to what extent tags are conceptually related to professionals’ keywords and it was found that tags of higher specificity tended to have a higher semantic relatedness to professionals’ keywords. This leads to the conclusion that the term’s power as a differentiator is related to its semantic relatedness to documents. The findings on tag attributes identified the important bibliographic attributes of tags beyond describing subjects or topics of a document. The findings also showed that tags have essential attributes matching those defined in FRBR. Furthermore, in terms of specific subject areas, the findings originally identified that taggers exhibited different tagging behaviors representing distinctive features and tendencies on web documents characterizing digital heterogeneous media resources. These results have led to the conclusion that there should be an increased awareness of diverse user needs by subject in order to improve metadata in practical applications. This dissertation research is the first necessary step to utilize social tagging in digital information organization by verifying the quality and efficacy of social tagging. This dissertation research combined both quantitative (statistics) and qualitative (content analysis using FRBR) approaches to vocabulary analysis of tags which provided a more complete examination of the quality of tags. Through the detailed analysis of tag properties undertaken in this dissertation, we have a clearer understanding of the extent to which social tagging can be used to replace (and in some cases to improve upon) professional indexing.

Recomendr-entity recommendation based on ad-hoc dimensions

The growing availability and popularity of opinion rich resources on the online web resources, such as review sites and personal blogs, has made it convenient to find out about the opinions and experiences of layman people. But, simultaneously, this huge eruption of data has made it difficult to reach to a conclusion. In this thesis, I develop a novel recommendation system, Recomendr that can help users digest all the reviews about an entity and compare candidate entities based on ad-hoc dimensions specified by keywords. It expects keyword specified ad-hoc dimensions/features as input from the user and based on those features; it compares the selected range of entities using reviews provided on the related User Generated Contents (UGC) e.g. online reviews. It then rates the textual stream of data using a scoring function and returns the decision based on an aggregate opinion to the user. Evaluation of Recomendr using a data set in the laptop domain shows that it can effectively recommend the best laptop as per user-specified dimensions such as price. Recomendr is a general system that can potentially work for any entities on which online reviews or opinionated text is available.