The evolution of data science and big data research: A bibliometric analysis

Data Science Curriculum in the iField

Many disciplines, including the broad Field of Information (iField), have been offering Data Science (DS) programs. There have been significant efforts exploring an individual discipline's identity and unique contributions to the broader DS education landscape. To advance DS education in the iField, the iSchool Data Science Curriculum Committee (iDSCC) was formed and charged with building and recommending a DS education framework for iSchools. This paper reports on the research process and findings of a series of studies to address important questions: What is the iField identity in the multidisciplinary DS education landscape? What is the status of DS education in iField schools? What knowledge and skills should be included in the core curriculum for iField DS education? What are the jobs available for DS graduates from the iField? What are the differences between graduate-level and undergraduate-level DS education? Answers to these questions will not only distinguish an iField approach to DS education but also define critical components of DS curriculum. The results will inform individual DS programs in the iField to develop curriculum to support undergraduate and graduate DS education in their local context.

Data mining topics in the discipline of library and information science: analysis of influential terms and Dirichlet multinomial regression topic model

Purpose

The purpose of this study is to explore to which extent data mining research would be associated with the library and information science (LIS) discipline. This study aims to identify data mining related subject terms and topics in representative LIS scholarly publications.

Design/methodology/approach

A large set of bibliographic records over 38,000 was collected from a scholarly database representing the fields of LIS and the data mining, respectively. A multitude of text mining techniques were applied to investigate prevailing subject terms and research topics, such as influential term analysis and Dirichlet multinomial regression topic modeling.

Findings

The findings of this study revealed the relationship between the LIS and data mining research domains. Various data mining method terms were observed in recent LIS publications, such as machine learning, artificial intelligence and neural networks. The topic modeling result identified prevailing data mining related research topics in LIS, such as machine learning, deep learning, big data and among others. In addition, this study investigated the trends of popular topics in LIS over time in the recent decade.

Originality/value

This investigation is one of a few studies that empirically investigated the relationships between the LIS and data mining research domains. Multiple text mining techniques were employed to delineate to which extent the two research domains would be associated with each other based on both at the term-level and topic-level analysis. Methodologically, the study identified influential terms in each domain using multiple feature selection indices. In addition, Dirichlet multinomial regression was applied to explore LIS topics in relation to data mining.

Studying the cognitive relatedness between topics in the global science landscape: The case of Big Data research

Taking Big Data research as a case study, this article intends to investigate the cognitive relatedness of research topics across the global science landscape to a focal topic. Several levels of cognitive relatedness are established depending on the citation distance between the citing publications and a core set of publications. The concept of citation generation is adopted for identifying and classifying other publications with different levels of relatedness to the core set. The micro publication-level classification system of Centre for Science and Technology Studies (CWTS) is applied for determining clusters of publication sets at the topic level. The overall cognitive relatedness of micro clusters to Big Data core publications are measured based on the mean citation generation of all the publications in corresponding clusters. In addition to the given clusters, this study also explores the ‘topics’ relatedness from a semantic point of view, by extracting high-frequency title terms of publications in each generation. Results show that data analysis methods and technologies are the topics with the strongest cognitive relatedness to Big Data research, while topics on physics and astronomy studies present the weakest relatedness. This approach allows assessment of relatedness between research topics by considering the citations distribution across multiple citation generations, and can provide useful insights to study and characterise topics with fuzzy boundaries or are difficult to delineate, thus representing a novel toolset relevant in the context of studying interdisciplinary research.

Using Social Media Analysis to Study Population Dietary Behaviours: A Scoping

BACKGROUND

The rapid adoption and sustained use of social media globally has provided researchers with access to unprecedented quantities of low-latency data at minimal costs. This may be of particular interest to nutrition research as food is frequently posted about and discussed on social media platforms. This scoping review investigates the ways in which social media is being used to understand population food consumption, attitudes, and behaviours.

METHODS

The peer-reviewed literature was searched from 2003 to 2021 using four electronic databases.

RESULTS

The review identified 71 eligible studies from 25 countries. Two thirds (n=47) were published within the last five years. The United States had the highest research output (31%, n=22) and Twitter was the most used platform (41%, n=29). A diverse range of dataset sizes were used, with some studies relying on manual techniques to collect and analyse data while others required the use of advanced software technology. Most studies were conducted by disciplines outside health with only two studies (3%) conducted by nutritionists.

CONCLUSION

It appears the development of methodological and ethical frameworks as well as partnerships between experts in nutrition and information technology may be required to advance the field in nutrition research. Moving beyond traditional methods of dietary data collection may prove social media as a useful adjunct to inform recommended dietary practices and food policies. This article is protected by copyright. All rights reserved.

The Nexus between Big Data and Sustainability: An Analysis of Current Trends and Developments

With the development of technological innovations, Big Data is transforming the socio-economic world, impacting almost every organization and person. The transformations associated with the development of Big Data have important consequences for the sustainability of organizations, regions, and the society as a whole, and as such, they have been specifically addressed by the academic literature focusing on sustainability. Despite its importance, and perhaps because of its rapid emergence, there is a lack of studies dealing with the analysis of this body of literature and its trends. The current research attempts to fill this gap. The study develops a bibliometric and visualization analysis of the literature on the nexus between Big Data and Sustainability. The research analyzes 726 documents on this topic, published until the end of 2020, in the Web of Science Core Collection database through the VOSviewer software. The results indicate the main trends and developments on the topic related to the most cited papers, authors, publications, institutions, and countries. The visualized frameworks, structures and trends are useful for both researchers and practitioners, as they can help them understand the current situation, issues to consider, and main developments on the topic.