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Recently, the importance and effectiveness of Wikipedia Mining has been shown in several researches. One popular research area on Wikipedia Mining focuses on semantic relatedness measurement, and research in this area has shown that Wikipedia can be used for semantic relatedness measurement. However, previous methods are facing two problems; accuracy and scalability. To solve these problems, the purpose of this paper is to propose an efficient semantic relatedness measurement method that leverages global statistical information of Wikipedia. Furthermore, a new test collection is constructed based on Wikipedia concepts for evaluating semantic relatedness measurement methods.

The authors' approach leverages global statistical information of the whole Wikipedia to compute semantic relatedness among concepts (disambiguated terms) by analyzing co‐occurrences of link pairs in all Wikipedia articles. In Wikipedia, an article represents a concept and a link to another article represents a semantic relation between these two concepts. Thus, the co‐occurrence of a link pair indicates the relatedness of a concept pair. Furthermore, the authors propose an integration method with tfidf as an improved method to additionally leverage local information in an article. Besides, for constructing a new test collection, the authors select a large number of concepts from Wikipedia. The relatedness of these concepts is judged by human test subjects.

An experiment was conducted for evaluating calculation cost and accuracy of each method. The experimental results show that the calculation cost of this approach is very low compared to one of the previous methods and more accurate than all previous methods for computing semantic relatedness.

This is the first proposal of co‐occurrence analysis of Wikipedia links for semantic relatedness measurement. The authors show that this approach is effective to measure semantic relatedness among concepts regarding calculation cost and accuracy. The findings may be useful to researchers who are interested in knowledge extraction, as well as ontology researches.

The purpose of this paper is to propose a method to detect local events in real time using Twitter, an online microblogging platform. The authors especially aim at detecting local events regardless of the type and scale.

The method is based on the observation that relevant tweets (Twitter posts) are simultaneously posted from the place where a local event is happening. Specifically, the method first extracts the place where and the time when multiple tweets are posted using a hierarchical clustering technique. It next detects the co-occurrences of key terms in each spatiotemporal cluster to find local events. To determine key terms, it computes the term frequency-inverse document frequency (TFIDF) scores based on the spatiotemporal locality of tweets.

From the experimental results using geotagged tweet data between 9 a.m. and 3 p.m. on October 9, 2011, the method significantly improved the precision of between 50 and 100 per cent at the same recall compared to a baseline method.

In contrast to existing work, the method described in this paper can detect various types of small-scale local events as well as large-scale ones by incorporating the spatiotemporal feature of tweet postings and the text relevance of tweets. The findings will be useful to researchers who are interested in real-time event detection using microblogs.

The web is now a significant component of the recruitment and job search process. However, very little is known about how companies and job seekers use the web, and the ultimate effectiveness of this process. The specific research questions guiding this study are: how do people search for job‐related information on the web? How effective are these searches? And how likely are job seekers to find an appropriate job posting or application?

The data used to examine these questions come from job seekers submitting job‐related queries to a major web search engine at three points in time over a five‐year period.

Results indicate that individuals seeking job information generally submit only one query with several terms and over 45 percent of job‐seeking queries contain a specific location reference. Of the documents retrieved, findings suggest that only 52 percent are relevant and only 40 percent of job‐specific searches retrieve job postings.

This study provides an important contribution to web research and online recruiting literature. The data come from actual web searches, providing a realistic glimpse into how job seekers are actually using the web.

The results of this research can assist organizations in seeking to use the web as part of their recruiting efforts, in designing corporate recruiting web sites, and in developing web systems to support job seeking and recruiting.

This research is one of the first studies to investigate job searching on the web using longitudinal real world data.

Although the business model field of study has been a focus of attention for both researchers and practitioners within the past two decades, it still suffers from concern about its identity. Accordingly, this paper aims to clarify the intellectual structure of business model through identifying the research clusters and their sub-clusters, the prominent relations and the dominant research trends.

This paper uses some common text mining methods including co-word analysis, burst analysis, timeline analysis and topic modeling to analyze and mine the title, abstract and keywords of 14,081 research documents related to the domain of business model.

The results revealed that the business model field of study consists of three main research areas including electronic business model, business model innovation and sustainable business model, each of which has some sub-areas and has been more evident in some particular industries. Additionally, from the time perspective, research issues in the domain of sustainable development are considered as the hot and emerging topics in this field. In addition, the results confirmed that information technology has been one of the most important drivers, influencing the appearance of different study topics in the various periods.

The contribution of this study is to quantitatively uncover the dominant knowledge structure and prominent research trends in the business model field of study, considering a broad range of scholarly publications and using some promising and reliable text mining techniques.

This study systematically reviews the current state of research on the application of high technology in supply chain management (SCM). It identifies key topics, trends and influential scholars in this field, providing a knowledge structure for future research. This study contributes to advancing the understanding of how high technology can be leveraged to enhance SCM, guiding and informing future research endeavors.

A comprehensive bibliometric analysis was conducted on 1,523 published articles retrieved from Web of Science. Through co-occurrence analysis of the titles, abstracts and keywords, the authors investigated popular research trends and topics. Through co-citation and co-authorship analyses, the authors identified leading research clusters, productive researchers and countries of the research.

There is a significant increase in publications by scholars from the USA, China and India on the impact of high technology on supply chains, particularly on food supply chains. Most articles examine the barriers and challenges of applying blockchain technology to different aspects of supply chains. Active contributions predominantly originate from scholars in the USA and China. The top five largest clusters are “supply chain management,” “scoping review,” “blockchain technology”, “food supply chains” and “management perception.”

This study represents the first systematic review establishing a comprehensive framework of knowledge on high technology and supply chains. Highlighting key research areas, tracing the evolution of research and explaining the knowledge structures pertaining to the role of high technology in supply chains, this study contributes to the existing literature and its findings hold practical implications for scholars and practitioners.

The current study focuses on many risk categories that have emerged in the digital ecosystem of the financial technology industry, which has dramatically changed traditional financial systems as a result of innovations in financial technology.

The Web of Science Core Collection database was used to find a data set of 719 pertinent papers on the subject encompassing the year 2015–2023. The sample procedure was carried out utilising the PRISMA approach. The keywords were first gathered relating to technological risks in banking sectors and after confirming the keywords, the authors performed the search by the “topic” which covers “title” in the search bar. On February 15, 2023, the Web of Science database was searched using the terms “Cyber security risk OR data theft OR financial crimes OR financial stability risk OR operational risk OR default risk OR money laundering OR financial terrorism AND FinTech AND banking sector”. Two-step approach is applied in this study. First, descriptive analysis is applied using RStudio to highlight prominent authors, countries and affiliations. Furthermore, relationship among authors, countries and keywords is shown by using three fields plot. Second, using VOSviewer, co-occurrence of keyword analysis is used to determine the most influential themes.

The findings show that 2,611 documents have been published from 2016 to 2023. Year 2021 is the most productive year in terms of number of publications. The results also show that WANG XC is tied for the position of most prolific contributing author. In a similar vein, the United States leads the world in publication output. Furthermore, Southwestern University of Finance and Economics in China is leading the list with 15 articles. The results from the co-occurrence of keywords reveal that “default risk”, “operational risk”, “money laundering”, “credit risk”, “corporate governance”, “systematic risk”, “financial stability risk”, “risk management” and “crises” are the frequently keywords.

The results of this study are beneficial to academia and industry in order to advance their current understanding of FinTech and associated concerns. This work expands the understanding of the technology hazards facing the banking industry from a broad perspective.

To propose a categorization of the different conflation procedures at the two basic approaches, non‐linguistic and linguistic techniques, and to justify the application of normalization methods within the framework of linguistic techniques.

Presents a range of term conflation methods, that can be used in information retrieval. The uniterm and multiterm variants can be considered equivalent units for the purposes of automatic indexing. Stemming algorithms, segmentation rules, association measures and clustering techniques are well evaluated non‐linguistic methods, and experiments with these techniques show a wide variety of results. Alternatively, the lemmatisation and the use of syntactic pattern‐matching, through equivalence relations represented in finite‐state transducers (FST), are emerging methods for the recognition and standardization of terms.

The survey attempts to point out the positive and negative effects of the linguistic approach and its potential as a term conflation method.

Outlines the importance of FSTs for the normalization of term variants.

Since the second world war, considerable research funds and effort have been spent on developing means for controlling the ever‐increasing flood of recorded knowledge. As far as librarians and information officers are concerned, the problem can be divided up into five distinct stages, as shown in Figure 1.

This study aims to update and extend previous efforts gauging the status of the quickly evolving field of digital humanities (DH). Based on a sample of directly relevant DH literature during 2005–2020 from Web of Science, the study conducts a longitudinal examination of the research output, intellectual structures and contributors.

The study applies bibliometric methods, social network analysis and visualization tools to conduct a longitudinal examination.

The research output and scope of DH topics has grown over time with a widening and deepening field in four major development stages. Through both term frequency and term co-occurrence relationship networks, this study further identifies four major reoccurring topics and themes of DH research: (1) collections and contents; (2) technologies, techniques, theories and methods; (3) collaboration, interdisciplinarity and support and (4) DH evolution. Finally, leading DH research contributors (authors, institutions and nations) are also identified.

This study utilizes a greater number of and richer subject sources than previous efforts to identify the overall intellectual structures of DH research based on key terms from titles, abstracts and author keywords. It expands on previous efforts and furthers our understanding of DH research with more recent DH literature and richer subject sources from the literature.

Even though researchers have discussed gender stereotype change, only a few studies have specifically projected outcomes or consequences. Hence, the main purpose of this study is to examine the impact of gender stereotype change concerning the different outcomes.

In achieving the purpose, the authors searched and reviewed current empirical knowledge on the outcomes of gender stereotype change in the Scopus and EBSCOhost databases from 1970 to 2020. The entire process was conducted through a systematic literature review methodology. The article selection criteria were executed using the PRISMA article selection flowchart steps, and 15 articles were included for the review.

The findings reveal that the outcomes from gender stereotype change research can be categorized mainly under the themes of “family and children,” “marriage” and “equality and women's employment.”

The co-occurrence network visualization map reveals gaps in the existing literature. There may be more possible outcomes relating to the current realities, and more cross-cultural research is needed.

These outcomes provide some implications for policymakers.

Even though researchers have discussed gender stereotype change on its various outcomes or consequences, research is less. Hence, this study provides a synthesis of consequences and addresses the gaps in the area.