The state of research on folksonomies in the field of Library and Information Science : a Systematic Literature Review

Purpose – The purpose of this thesis is to provide an overview of all relevant peer-reviewed articles on folksonomies, social tagging and social bookmarking as knowledge organisation systems within the field of Library and Information Science by reviewing the current state of research on these systems of managing knowledge. Method – I use the systematic literature review method in order to systematically and transparently review and synthesise data extracted from 39 articles found through the discovery system LUBsearch in order to find out which, and to which degree different methods, theories and systems are represented, which subfields can be distinguished, how present research within these subfields is and which larger conclusions can be drawn from research conducted between 2003-2013 on folksonomies. Findings – There have been done many studies which are exploratory or reviewing literature discussions, and other frequently used methods which have been used are questionnaires or surveys, although often in conjunction with other methods. Furthermore, out of the 39 studies, 22 were quantitative, 15 were qualitative and 2 used mixed methods. I also found that there were an underwhelming number of theories being explicitly used, where merely 11 articles explicitly used theories, and only one theory was used twice. No key authors on the topic were identified, though Knowledge Organization, Information Processing & Management and Journal of the American Society for Information Science and Technology were recognised as key journals for research on folksonomies. There have been plenty of studies on how tags and folksonomies have effected other knowledge organisation systems, or how pre-existing have been used to create new systems. Other well represented subfields include studies on the quality or characteristics of tags or text, and studies aiming to improve folksonomies, search methods or tags. Value – I provide an overview on what has been researched and where the focus on said research has been during the last decade and present future research suggestions and identify possible dangers to be wary of which I argue will benefit folksonomies and knowledge organisation as a whole

Soft peer review: social software and distributed scientific evaluation

The debate on the prospects of peer-review in the Internet age and the increasing criticism leveled against the dominant role of impact factor indicators are calling for new measurable criteria to assess scientific quality. Usage-based metrics offer a new avenue to scientific quality assessment but face the same risks as first generation search engines that used unreliable metrics (such as raw traffic data) to estimate content quality. In this article I analyze the contribution that social bookmarking systems can provide to the problem of usage-based metrics for scientific evaluation. I suggest that collaboratively aggregated metadata may help fill the gap between traditional citation-based criteria and raw usage factors. I submit that bottom-up, distributed evaluation models such as those afforded by social bookmarking will challenge more traditional quality assessment models in terms of coverage, efficiency and scalability. Services aggregating user-related quality indicators for online scientific content will come to occupy a key function in the scholarly communication system

Aggregation operators in group decision making: Identifying citation classics via H-classics

To analyze the past, present and future of a particular research field, classic papers are usually studied because they identify the highly cited papers being a relevant reference point in that specific research area. As a result of the possible mapping between high quality research and high citation counts, highly cited papers are very interesting. The objective of this study is to use the H-classics method, which is based on the popular h-index, to identify and analyze the highly cited documents published about aggregation operators in the research area of group decision making. According to the H-classics method, this research area is represented by 87 citation classics, which have been published from 1988 to 2014. Authors, affiliations (universities/institutions and countries), journals, books and conferences, and the topics covered by these 87 highly cited papers are studied.The authors would like to thank FEDER financial support from the Projects TIN2013-40658-P and TIN2016- 75850-P

Consensus in a fuzzy environment: a bibliometric study

In today’s organizations, group decision making has become a part of everyday organizational life. It involves multiple individuals interacting to reach a decision. An important question here is the level of agreement or consensus achieved among the individuals before making the decision. Traditionally, consensus has been meant to be a full and unanimous agreement. However, it is often not reachable in practice. A more reasonable approach is the use of softer consensus measures, which assess the consensus in a more flexible way, reflecting the large spectrum of possible partial agreements and guiding the discussion process until widespread agreement is achieved. As soft consensus measures are more human-consistent in the sense that they better reflect a real human perception of the essence of consensus, consensus models based on these kind of measures have been widely proposed. The aim of this contribution is to present a bibliometric study performed on the consensus approaches that have been proposed in a fuzzy environment. It gives an overview about the research products gathered in this research field. To do so, several points have been studied, among others: countries, journals, top contributing authors, most cited keywords, papers and authors. This allows us to show a quick shot of the state of the art in this research area

International Comparative Domain Analysis in Knowledge Organization Research Topics in Four Countries - Brazil, South Korea, Spain and the United States

This study aims to identify and compare the domains of knowledge organization from four countries: Brazil, South Korea, Spain and the United States. Four researchers from diverse backgrounds study investigate knowledge organization (KO) on an international scale using domain analysis of keywords from journal articles. Each country selected two journals in LIS and evaluated each article to find those related to KO. The findings show there are some similarity in an international level and difference in a national level of knowledge organization domain. 21 overlapped topics across four countries have been identified. In addition, the findings show some examples of unique research topics of KO domain from each country. This international comparative domain analysis study can contribute to promote academic communication amongst KO researchers and bring more international collaborative research opportunities

Relationships between Metadata application and downloads in an institutional repository of an American Law School

© 2019, The authors. Background. The Duke Law Scholarship Repository is a successful digital repository of an American law school, with over 1 million downloads per year. A series of studies were conducted to understand the relationship between metadata work and downloads. Objective. The paper reports an analysis of the relationships between certain metadata elements and repository downloads. Methods. Quantitative statistical methods, specifically correlation, t-test and multiple regression analysis, were used. Results. Statistically significant relationships were found between download frequency and factors relating to abstract, co-authors, page count and discipline. Negative statistically significant relationships were found between download frequency and free text keywords, as well as controlled vocabulary subject terms. Contributions. This study is an example of how in-use repository system administrators can demonstrate the impact of metadata work for institutional scholarly outreach. Also, this study adds another dimension to the keyword and searching/download literature that has been building since the 1970s

Big data and social media: A scientometrics analysis

The purpose of this research is to investigate the status and the evolution of the scientific studies for the effect of social networks on big data and usage of big data for modeling the social net-works users’ behavior. This paper presents a comprehensive review of the studies associated with big data in social media. The study uses Scopus database as a primary search engine and covers 2000 of highly cited articles over the period 2012-2019. The records are statistically analyzed and categorized in terms of different criteria. The findings show that researches have grown exponentially since 2014 and the trend has continued at relatively stable rates. Based on the survey, decision support systems is the keyword which has carried the highest densities followed by heuristics methods. Among the most cited articles, papers published by researchers in United States have received the highest citations (7548), followed by United Kingdom (588) and China with 543 citations. Thematic analysis shows that the subject nearly maintained an important and well-developed research field and for better results we can merge our research with “big data analytics” and “twitter” that are important topics in this field but not developed well

Structural review of relics tourism by text mining and machine learning

Purpose: The objective of the paper is to find trends of research in relic tourism-related topics. Specifically, this paper uncovers all published studies having latent issues with the keywords "relic tourism" from the Web of Science database. Methods: A total of 109 published articles (2002-2021) were collected related to "relic tourism." Machine learning tools were applied. Network analysis was used to highlight top researchers in this field, their citations, keyword clusters, and collaborative networks. Text analysis and Bidirectional Encoder Representation from Transformer (BERT) of artificial intelligence model were used to predict text or keyword-based topic reference in machine learning. Results: All the papers are published basically on three primary keywords such as "!relics," "culture," and "heritage." Secondary keywords like "protection" and "development" also attract researchers to research this topic. The co-author network is highly significant for diverse authors, and geographically researchers from five countries are collaborating more on this topic. Implications: Academically, future research can be predicated with dense keywords. Journals can bring more special issues related to the topic as relic tourism still has some unexplored areas