Bibliometric analysis and literature review of ecotourism: Toward sustainable development

In recent decades, rising consumer interest in visiting relatively less commercialized natural destinations has facilitated the growth of ecotourism. Yet the research on ecotourism is fragmented, presenting gaps in the current understanding of this topic. This study performs a bibliometric analysis to assimilate the present knowledge from a total of 878 articles published in six reputable outlets between 1990 and 2019. The study analyzed citation chains and coauthorship networks to acknowledge contributions from select authors, organizations, and countries. Next, a cocitation analysis of the prior literature identified four major thematic areas: ecological preservation, residents' interests, the carbon footprint, and tourists' behaviors. Further, a dynamic cocitation analysis technique mapped the development of these thematic areas. Subsequently, a content analysis of the four thematic areas delivered significant insights about prior research in the domain and indicated future avenues of research.publishedVersio

Learning Reputation in an Authorship Network

The problem of searching for experts in a given academic field is hugely important in both industry and academia. We study exactly this issue with respect to a database of authors and their publications. The idea is to use Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA) to perform topic modelling in order to find authors who have worked in a query field. We then construct a coauthorship graph and motivate the use of influence maximisation and a variety of graph centrality measures to obtain a ranked list of experts. The ranked lists are further improved using a Markov Chain-based rank aggregation approach. The complete method is readily scalable to large datasets. To demonstrate the efficacy of the approach we report on an extensive set of computational simulations using the Arnetminer dataset. An improvement in mean average precision is demonstrated over the baseline case of simply using the order of authors found by the topic models

Bibliometric analysis and literature review of ecotourism: Toward sustainable development

In recent decades, rising consumer interest in visiting relatively less commercialized natural destinations has facilitated the growth of ecotourism. Yet the research on ecotourism is fragmented, presenting gaps in the current understanding of this topic. This study performs a bibliometric analysis to assimilate the present knowledge from a total of 878 articles published in six reputable outlets between 1990 and 2019. The study analyzed citation chains and coauthorship networks to acknowledge contributions from select authors, organizations, and countries. Next, a cocitation analysis of the prior literature identified four major thematic areas: ecological preservation, residents' interests, the carbon footprint, and tourists' behaviors. Further, a dynamic cocitation analysis technique mapped the development of these thematic areas. Subsequently, a content analysis of the four thematic areas delivered significant insights about prior research in the domain and indicated future avenues of research

Measuring Author Research Relatedness: A Comparison of Word-based,Topic-based and Author Cocitation Approaches

Relationships between authors based on characteristics of published literature have been studied for decades. Author cocitation analysis using mapping techniques has been most frequently used to study how closely two authors are thought to be in intellectual space based on how members of the research community co-cite their works. Other approaches exist to study author relatedness based more directly on the text of their published works. In this study we present static and dynamic word-based approaches using vector space modeling, as well as a topic-based approach based on Latent Dirichlet Allocation for mapping author research relatedness. Vector space modeling is used to define an author space consisting of works by a given author. Outcomes for the two word-based approaches and a topic-based approach for 50 prolific authors in library and information science are compared with more traditional author cocitation analysis using multidimensional scaling and hierarchical cluster analysis. The two word-based approaches produced similar outcomes except where two authors were frequent co-authors for the majority of their articles. The topic-based approach produced the most distinctive map

Scientometric Analysis of Optimisation and Machine Learning Publications

Introduction: Optimisation is an important aspect of machine learning because it helps improve accuracy and reduce errors in the model's predictions. Purpose: The purpose of this research is to identify the global structure of optimization and machine learning. The work specifically looks at the collaborative network of countries in these fields, the top 20 authors in terms of production from 2015–2021, and the co-citation network of articles. Methodology: In this study, co-word analysis and social network analysis were used to conduct a descriptive study based on the scientometric approach and the content analysis method. In this research, around 17,500 articles on optimization and machine learning published between 2015 and 2021 were extracted. An ANOVA was performed to evaluate whether there was a significant difference between betweenness, closeness, and pagerank. The Dimensions database was utilised for the investigation without language constraints. Moreover, Bibliometrix was used for calculation and visualization. Findings: The results revealed a substantial difference between betweenness, proximity, and pagerank, indicating that this research has the potential to bring vital insights into future optimization and machine learning research

Development of Computer Science Disciplines - A Social Network Analysis Approach

In contrast to many other scientific disciplines, computer science considers conference publications. Conferences have the advantage of providing fast publication of papers and of bringing researchers together to present and discuss the paper with peers. Previous work on knowledge mapping focused on the map of all sciences or a particular domain based on ISI published JCR (Journal Citation Report). Although this data covers most of important journals, it lacks computer science conference and workshop proceedings. That results in an imprecise and incomplete analysis of the computer science knowledge. This paper presents an analysis on the computer science knowledge network constructed from all types of publications, aiming at providing a complete view of computer science research. Based on the combination of two important digital libraries (DBLP and CiteSeerX), we study the knowledge network created at journal/conference level using citation linkage, to identify the development of sub-disciplines. We investigate the collaborative and citation behavior of journals/conferences by analyzing the properties of their co-authorship and citation subgraphs. The paper draws several important conclusions. First, conferences constitute social structures that shape the computer science knowledge. Second, computer science is becoming more interdisciplinary. Third, experts are the key success factor for sustainability of journals/conferences

Aggregating Content and Network Information to Curate Twitter User Lists

Twitter introduced user lists in late 2009, allowing users to be grouped according to meaningful topics or themes. Lists have since been adopted by media outlets as a means of organising content around news stories. Thus the curation of these lists is important - they should contain the key information gatekeepers and present a balanced perspective on a story. Here we address this list curation process from a recommender systems perspective. We propose a variety of criteria for generating user list recommendations, based on content analysis, network analysis, and the "crowdsourcing" of existing user lists. We demonstrate that these types of criteria are often only successful for datasets with certain characteristics. To resolve this issue, we propose the aggregation of these different "views" of a news story on Twitter to produce more accurate user recommendations to support the curation process

Introducing CitedReferencesExplorer (CRExplorer): A program for Reference Publication Year Spectroscopy with Cited References Standardization

We introduce a new tool - the CitedReferencesExplorer (CRExplorer, www.crexplorer.net) - which can be used to disambiguate and analyze the cited references (CRs) of a publication set downloaded from the Web of Science (WoS). The tool is especially suitable to identify those publications which have been frequently cited by the researchers in a field and thereby to study for example the historical roots of a research field or topic. CRExplorer simplifies the identification of key publications by enabling the user to work with both a graph for identifying most frequently cited reference publication years (RPYs) and the list of references for the RPYs which have been most frequently cited. A further focus of the program is on the standardization of CRs. It is a serious problem in bibliometrics that there are several variants of the same CR in the WoS. In this study, CRExplorer is used to study the CRs of all papers published in the Journal of Informetrics. The analyses focus on the most important papers published between 1980 and 1990.Comment: Accepted for publication in the Journal of Informetric