Computer Science Papers in Web of Science: A Bibliometric Analysis

Are Papers Asking Questions Cited More Frequently in Computer Science?

In this article, we test the hypothesis that computer science papers asking questions (i.e., those with a question mark at the end of their title) are cited more frequently than those that do not have this property. To this end, we analyze a data set of almost two million records on computer science papers indexed in the Web of Science database and focus our investigation on the mean number of citations per paper of its specific subsets. The main finding is that the average number of citations per paper of the so-called “asking papers” is greater by almost 20% than that of other papers, and that this difference is statistically significant.

An Exploratory Study of the Research on Caregiver Depression: Using Bibliometrics and LDA Topic Modeling

Purpose: The purpose of this paper is to provide readers with a comprehensive overview of scholarly work on the depression of caregivers using bibliometrics and text mining. Methods: A total of 426 articles published between 2000 and 2018 were retrieved from the Clarivate Analytics Web of Science, and then, computer-aided bibliometric analysis as well as Latent Dirichlet Allocation (LDA) topic modeling were conducted on the collection of the data. Results: Descriptive statistics on the increasing number of publications, network analysis of scientific collaboration between countries, word co-occurrence analysis, conceptual structure, and six latent topics (k = 6) identified are discussed. Conclusions: Preventing or managing depression among caregivers is a growing field with the highest priority for the aging population. In the future, collaborating between countries and reflecting cultural backgrounds in caregiver depression research are needed. This study is expected to contribute to the field of psychological distress of caregivers in looking a big picture of the current position through data-driven analysis and moving forward towards a better direction.

A Scientometric Study of Neurocomputing Publications (1992–2018): An Aerial Overview of Intrinsic Structure

The international journal of neurocomputing (NC) is considered to be one of the most sought out journals in the computer science research fraternity. In this paper, an extensive bibliometric overview of this journal is performed. The bibliometric data is extracted from the Web of Science (WoS) repository. The main objective of this study is to reveal internal structures and hidden inferences, such as highly productive and influential authors, most contributing countries, top institutions, collaborating authors, and so on. The CiteSpace and VOS viewer is used to visualize the graphical mapping of the bibliometric data. Further, the document co-citations network, cluster detection and references with strong citation burst is analyzed to reveal the intellectual base of NC publications.