From chunks to clusters: Identifying similarity features in social discussion

Users’ reviews on social media are crucial to understanding users’ interests and their opinions. Although there has been sufficient research on online reviews about products and services, there has been a lack of studies examining online reviews about books. This study extends our earlier work on frequency analysis of review words on online book reviews, which identified users’ interests in discussing books by analyzing the frequency of words users used in their book reviews on a social networking site. This paper intends to investigate whether the frequencies of the review words would represent similarities that would help understand the characteristics of books in selecting books for children. This study performs hierarchical cluster analysis on the selected books to identify homogeneous clusters of cases (books) based on selected characteristics (word frequencies). The finding of this study shows meaningful similarities in the social discussion by clustering books based on the characteristics of books. The results of this study help us understand the specific features of books and user behavior in discussing books on a social networking site. This study has implications for providing practical insights into the intrinsic values of users’ social discussion in identifying similarities among books

Mining users’ interests in discussing books on social media

Libraries have used book reviews to support their decision on book selection and collection building. Many researchers proved that online reviews are helpful for the decision on product or service purchases. However, little research has been done on online book reviews about their usefulness in selecting children. This study conducts a frequency analysis of words applied to the categories identified based on the Latent Dirichlet Allocation topic model. For the analysis of online book reviews, this study selects sample books from the recommended reading lists from the American Library Association (ALA). It collects reviews about the books from Goodreads. This study investigates whether the patterns of word frequency would show interesting points which reflect the characteristics and features of books. This study performs a hierarchical cluster analysis on the selected books and further examined reviews by conducting a content analysis on review texts. This study’s findings will identify the aspects of a book that users are concerned about in reviewing the books. Future research will take further steps in the investigation of the relationship between the word frequency and the features of books

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

A Multi-Aspect Topical Analysis of User-Generated Content

As analyzing and understanding users’ online reviews has increasingly become an essential part of the business decision, there has been sufficient research on online reviews about products and services. However, there have been few studies done on the usefulness of online book reviews for understanding users’ interests in discussing books. This study is part of a larger research project that aims to investigate whether online reviews on children’s books would represent significant factors in selecting books for children. This study extends our previous research on the topical analysis of online reviews on Goodreads.com. In this study, we aim to identify users’ interests in discussing books by analyzing the frequency of words that users used in their book reviews. This study also examines whether the patterns of word frequency would help understand the features of books. The findings of this study contribute to identifying multiaspect topics of a book that users are concerned about in reviewing the book. This study has implications for providing practical insights into the intrinsic values of users’ book reviews at the social networking site

Identifying Facets of Reader-Generated Online Reviews of Children’s Books Based on a Textual Analysis Approach

With the increasing popularity of social media, online reviews have become one of the primary information sources for book selection. Prior studies have analyzed online reviews, mostly in the domain of business. However, little research has examined the content of online book reviews of children’s books. Book reviews generated by book readers contain different aspects of information, such as opinions, feedback, or emotional responses, from the perspectives of readers. This study explores what aspects of the books are addressed in readers’ reviews, and then it intends to identify categorical features or facets of online book reviews of children’s books. We employed a textual analysis approach including the latent Dirichlet allocation topic modeling to analyze the content of book reviews. The results indicate that online book reviews exhibit different facets of the books, which can be used as access points by potential readers to help them select relevant books

Users’ hidden needs: An investigation of information sharing behaviors on online participatory platforms

As a marketing strategy, Word-of-Mouth is a valuable source which affects the decision before making the purchase, and its significant influence on consumers’ buying decision has been prominently discussed in such areas as marketing and advertising. Online reviews can be referred to as the “New Word-of-Mouth” in the Digital Age. Word-of-Mouth became more critical, as the quality of online reviews is relatively credible. Despite the proven evidence of online reviews as a successful strategy for supporting a decision for purchasing products, the helpfulness of online reviews has not been well understood within the Library communities. This study examines the helpfulness of online reviews as the new Word-of-Mouth to investigate whether online reviews would be useful to understand user needs in selecting books. Understanding user needs and the patterns in sharing information about books is significant in organizing and providing effective access to resources in libraries. This study aims to classify the characteristics associated with user sentiments and attitudes to model behavior features on online platforms. This study constructs a user behavior model by comparing reviews on two different online participatory platforms, Goodreads and Amazon. This study narrowly defines the scope of our investigation on online reviews of children’s books. Sample reviews were collected from the two sites based on the Newbery Medal Winners list by the American Library Association. The hypotheses investigating the relationships between the two platforms will be presented. This study will contribute to user studies regarding illustrating underlying patterns and trends of users’ information sharing behaviors

Vocabulary Integration Reexamined

Several thesauri have been published in various domains, or in the same subject domain. This heterogeneity caused the significant incompatibility of transferring or sharing data among different systems and databases. Therefore, thesaurus integration is a solution for handling this incompatibility issue. To achieve interoperability between different thesauri, mapping systems have been developed for establishing equivalents between terms in different thesauri. However, there is still ambiguity in term semantics and hierarchical relations used in thesauri. The purpose of this paper is to reexamine the issues and challenges in vocabulary mapping and integration between different controlled vocabulary systems. The paper outlines the history of the study of vocabulary mapping efforts and suggests a way in which the emerging practices on semantic issues and mapping problems can be articulated

Building a culture-rich environment in the organization and selection of books: An analysis of GoodReads reader reviews on multicultural books for children

It is essential that all children see themselves reflected in collections, services and programs developed by librarians. Multicultural literature serves as a powerful tool in enabling children to gain a better understanding of both their own culture and the cultures of others. Through this deeper knowledge, relationships can be strengthened, bridging the gap between children from diverse cultural backgrounds. In order to make informed decisions, librarians have used book reviews and award lists from authoritative sources to guide book selection decisions. As GoodReads, a social website for “readers and book recommendations,” has become more popular, the online reader ratings and reviews have received a great deal of attention in the business domain due to their perceived influence on customer purchasing decisions. Few studies have been done that look at the potential of online book reviews such as those shared by readers on GoodReads for library book selection and organization. Given the potential commercial, library, and academic interest in GoodReads, this research seeks to identify insights into reader ratings and reviews that analysis could provide. To ascertain the significance of the customer ratings and reviews on Goodreads we will investigate subjectivity and polarity of online reviews with neutral, positive, or negative sentiment with a list of books selected for American Library Association (ALA) Youth Media Honors and Awards for multicultural literature (Pura Belpré, Coretta Scott King, Schneider, and Stonewall). The study contributes to increasing awareness of the potential and challenges associated with using online reader reviews for library book selection

To share or not to share?: a comparative analysis of data sharing factors by different academic positions

The significance of sharing research data has been critically discussed since data sharing is an essential matter in scientific research where the process needs to create and use the vast amount of data in data-intensive research environment. The institutional or disciplinary context is important to understand research practices and data re-use in scientific fields which encourages collaboration among researchers and promotes their research. However, there have been little studies on data sharing behaviors within institutional or disciplinary context. This study develops a research model based on the integration of institutional theory and the theory of planned behavior by focusing on the community norm of data sharing. This research investigates how essential data sharing factors including regulative pressure by journals, normative pressure, scholarly altruism, perceived career benefit and risk, and perceived effort differ across diverse academic positions such as graduate/post-doctoral researchers, and assistant, associate, and full professors. The survey was conducted with researchers in U.S. academic institutions in STEM disciplines, such as physical sciences, biological sciences, engineering, health sciences, and social sciences, and the total of 1,656 responses were collected. A Multivariate Analysis of Variance is employed to examine the hypothesized relationships between data sharing behaviors and academic positions in the research model. This research will provide theoretical and practical implications for encouraging data sharing in research communities and developing data sharing policies for funding agencies, journal publishers, and academic institutions

To share or not to share?: a comparative analysis of data sharing factors by different academic positions

The significance of sharing research data has been critically discussed since data sharing is an essential matter in scientific research where the process needs to create and use the vast amount of data in data-intensive research environment. The institutional or disciplinary context is important to understand research practices and data re-use in scientific fields which encourages collaboration among researchers and promotes their research. However, there have been little studies on data sharing behaviors within institutional or disciplinary context. This study develops a research model based on the integration of institutional theory and the theory of planned behavior by focusing on the community norm of data sharing. This research investigates how essential data sharing factors including regulative pressure by journals, normative pressure, scholarly altruism, perceived career benefit and risk, and perceived effort differ across diverse academic positions such as graduate/post-doctoral researchers, and assistant, associate, and full professors. The survey was conducted with researchers in U.S. academic institutions in STEM disciplines, such as physical sciences, biological sciences, engineering, health sciences, and social sciences, and the total of 1,656 responses were collected. A Multivariate Analysis of Variance is employed to examine the hypothesized relationships between data sharing behaviors and academic positions in the research model. This research will provide theoretical and practical implications for encouraging data sharing in research communities and developing data sharing policies for funding agencies, journal publishers, and academic institutions