Data Science Publication: Thirty-Six Years Lesson of Scientometric Review

Purnomo, Agung; Rosyidah, Elsa; Firdaus, Mega; Asitah, Nur; Septianto, Andre

doi:10.1109/icimtech50083.2020.9211192

Cited by 13 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result of its extensive research infrastructure and collaborative efforts, the USA has also been a leader in several other research fields such as communication research (Moreno-Delgado et al. , 2021), data science (Purnomo et al. , 2020), e-participation (Qi et al.…”

Section: Discussionmentioning

confidence: 99%

Quantifying global digital journalism research: a bibliometric landscape

Banshal

Verma

Yuvaraj

2022

LHT

View full text Add to dashboard Cite

PurposeThe purpose of this paper is to present a comprehensive analysis of the current status and development of the digital journalism field from 1987 to 2021 using the Dimensions database.Design/methodology/approachUsing the Dimensions.ai database, 1734 articles were identified through search strategies which were published from 1987 to 2021. The downloaded results were analysed using specific parameters with the help of bibliometric and science mapping tools: Biblioshiny, VOSviewer and CiteSpace. The key contributions of the present comprehensive bibliometric study of the digital journalism field can be seen in terms of the following aspects: (1) Publication analysis from the perspectives of publication growth, key journals, contributing authors, institutions and countries done through Biblioshiny package. (2) Citation network analysis from the perspective of co-citation structure of papers, authors, countries and institutions done through VOSviewer. (3) Timeline analysis and keywords burst detection to identify hotspots and research trends in digital journalism with the help of CiteSpace.FindingsThe first paper with the keyword digital journalism was published in the year 1989. From 2011 onwards, there has been growth in digital journalism literature. The most popular journal in digital journalism studies is Digital Journalism, Journalism, Journalism Practice, Journalism Studies. Lewis, S.C. has contributed the most number of papers in digital journalism. Further, authors from the countries the USA, Spain, Brazil and UK have contributed immensely. The citation network of authors, institutions and countries contributing to digital journalism studies has also been explored in the study. Through burst analysis, hot topics in digital journalism were identified.Originality/valueThe paper provides a complete overview of the growth of digital journalism literature published from 1987 to 2021. The originality of this work lies in the triangulation of Biblioshiny, VOSviewer and CiteSpace software to present various aspects of bibliometric study. Findings of the study can help the researchers to identify areas as well as journals, authors, institutions working actively in the field of digital journalism.

show abstract

Section: Discussionmentioning

confidence: 99%

Quantifying global digital journalism research: a bibliometric landscape

Banshal

Verma

Yuvaraj

2022

LHT

View full text Add to dashboard Cite

show abstract

“…In addition, several researchers have attempted to construct features from external resources to improve classification accuracy. For example, Purnomo et al [27] used external feature information such as Wikipedia and news sites to improve the accuracy of literature classification. Bhatt et al [28] used Medical Subject Headings (MeSH) as the basis for selecting key terms related to stem cells as feature vectors to characterize the literature.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Salazar-Reyna et al [30] described data mining, machine learning, and software tools as three overlapping subsystems of data science along two dimensions (i.e., experimental versus theoretical; descriptive versus prescriptive). Purnomo et al [27] developed a schematic representation of the major domains that comprise data science and analytics (i.e., domain knowledge, statistics/mathematics, and computer science) and their overlap (i.e., machine learning, biostatistics, and data science). The literature is replete with publications on the use of data science, data analytics, and machine learning algorithms in various application areas such as healthcare, business, decision making, and marketing.…”

Section: Experimental Validationmentioning

confidence: 99%

Scientometric Analysis and Classification of Research Using Convolutional Neural Networks: A Case Study in Data Science and Analytics

et al. 2022

View full text Add to dashboard Cite

With the increasing development of published literature, classification methods based on bibliometric information and traditional machine learning approaches encounter performance challenges related to overly coarse classifications and low accuracy. This study presents a deep learning approach for scientometric analysis and classification of scientific literature based on convolutional neural networks (CNN). Three dimensions, namely publication features, author features, and content features, were divided into explicit and implicit features to form a set of scientometric terms through explicit feature extraction and implicit feature mapping. The weighted scientometric term vectors are fitted into a CNN model to achieve dual-label classification of literature based on research content and methods. The effectiveness of the proposed model is demonstrated using an application example from the data science and analytics literature. The empirical results show that the scientometric classification model proposed in this study performs better than comparable machine learning classification methods in terms of precision, recognition, and F1-score. It also exhibits higher accuracy than deep learning classification based solely on explicit and dominant features. This study provides a methodological guide for fine-grained classification of scientific literature and a thorough investigation of its practice.

show abstract