Analysis of Computer Science Communities Based on DBLP

It is popular nowadays to bring techniques from bibliometrics and scientometrics into the world of digital libraries to analyze the collaboration patterns and explore mechanisms which underlie community development. In this paper we use the DBLP data to investigate the author's scientific career and provide an in-depth exploration of some of the computer science communities. We compare them in terms of productivity, population stability and collaboration trends.Besides we use these features to compare the sets of topranked conferences with their lower ranked counterparts.Comment: 9 pages, 7 figures, 6 table

Evolution in the number of authors of computer science publications

This article analyses the evolution in the number of authors of scientific publications in computer science (CS). This analysis is based on a framework that structures CS into 17 constituent areas, proposed by Wainer et al. (Commun ACM 56(8):67–63, 2013), so that indicators can be calculated for each one in order to make comparisons. We collected and mined over 200,000 article references from 81 conferences and journals in the considered CS areas, spanning a 60-year period (1954–2014). The main insights of this article are that all CS areas witness an increase in the average number of authors, in every decade, with just one slight exception. We ordered the article references by number of authors, in ascending chronological order and grouped them into decades. For each CS area, we provide a perspective of how many groups (1-author papers, 2-author papers and so on) must be considered to reach certain proportions of the total for that CS area, e.g., the 90th and 95th percentiles. Different CS areas require different number of groups to reach those percentiles. For all 17 CS areas, an analysis of the point in time in which publications with n+1 authors overtake the publications with n authors is presented. Finally, we analyse the average number of authors and their rate of increase.This work was supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013

The Academic Midas Touch: An Unconventional Scientometric for Evaluating Academic Excellence

The recognition of academic excellence is fundamental to the scientific and academic endeavor. In particular, academic scientometrics that are able to computationally capture academic excellence are of great interest. In this work, we propose and investigate an unconventional scientometric termed the Academic Midas Touch (AMT) that refers to a researcher's tendency to produce outstanding publications (i.e., golden publications). Using an extensive dataset of mathematicians, both award-winning and otherwise, we show that the AMT scientometric is a valid and arguably valuable scientometric for the distinction of academic excellence.Comment: 8 pages, 3 figure

Analysing the evolution of computer science events leveraging a scholarly knowledge graph: a scientometrics study of top-ranked events in the past decade

The publish or perish culture of scholarly communication results in quality and relevance to be are subordinate to quantity. Scientific events such as conferences play an important role in scholarly communication and knowledge exchange. Researchers in many fields, such as computer science, often need to search for events to publish their research results, establish connections for collaborations with other researchers and stay up to date with recent works. Researchers need to have a meta-research understanding of the quality of scientific events to publish in high-quality venues. However, there are many diverse and complex criteria to be explored for the evaluation of events. Thus, finding events with quality-related criteria becomes a time-consuming task for researchers and often results in an experience-based subjective evaluation. OpenResearch.org is a crowd-sourcing platform that provides features to explore previous and upcoming events of computer science, based on a knowledge graph. In this paper, we devise an ontology representing scientific events metadata. Furthermore, we introduce an analytical study of the evolution of Computer Science events leveraging the OpenResearch.org knowledge graph. We identify common characteristics of these events, formalize them, and combine them as a group of metrics. These metrics can be used by potential authors to identify high-quality events. On top of the improved ontology, we analyzed the metadata of renowned conferences in various computer science communities, such as VLDB, ISWC, ESWC, WIMS, and SEMANTiCS, in order to inspect their potential as event metrics

Scholarly event characteristics in four fields of science : a metrics-based analysis

One of the key channels of scholarly knowledge exchange are scholarly events such as conferences, workshops, symposiums, etc.; such events are especially important and popular in Computer Science, Engineering, and Natural Sciences.However, scholars encounter problems in finding relevant information about upcoming events and statistics on their historic evolution.In order to obtain a better understanding of scholarly event characteristics in four fields of science, we analyzed the metadata of scholarly events of four major fields of science, namely Computer Science, Physics, Engineering, and Mathematics using Scholarly Events Quality Assessment suite, a suite of ten metrics.In particular, we analyzed renowned scholarly events belonging to five sub-fields within Computer Science, namely World Wide Web, Computer Vision, Software Engineering, Data Management, as well as Security and Privacy.This analysis is based on a systematic approach using descriptive statistics as well as exploratory data analysis. The findings are on the one hand interesting to observe the general evolution and success factors of scholarly events; on the other hand, they allow (prospective) event organizers, publishers, and committee members to assess the progress of their event over time and compare it to other events in the same field; and finally, they help researchers to make more informed decisions when selecting suitable venues for presenting their work.Based on these findings, a set of recommendations has been concluded to different stakeholders, involving event organizers, potential authors, proceedings publishers, and sponsors. Our comprehensive dataset of scholarly events of the aforementioned fields is openly available in a semantic format and maintained collaboratively at OpenResearch.org. © 2020, The Author(s)

A scientometric analysis of deep learning approaches for detecting Fake News

The unregulated proliferation of counterfeit news creation and dissemination that has been seen in recent years poses a constant threat to democracy. Fake news articles have the power to persuade individuals, leaving them perplexed. This scientometric study examined 569 documents from the Scopus database between 2012 and mid-2022 to look for general research trends, publication and citation structures, authorship and collaboration patterns, bibliographic coupling, and productivity patterns in order to identify fake news using deep learning. For this study, Biblioshiny and VOSviewer were used. The findings of this study clearly demonstrate a trend toward an increase in publications since 2016, and this dissemination of fake news is still an issue from a global perspective. Thematic analysis of papers reveals that research topics related to social media for surveillance and monitoring of public attitudes and perceptions, as well as fake news, are crucial but underdeveloped, while studies on deep fake detection, digital contents, digital forensics, and computer vision constitute niche areas. Furthermore, the results show that China and the USA have the strongest international collaboration, despite India writing more articles. This paper also examines the current state of the art in deep learning techniques for fake news detection, with the goal of providing a potential roadmap for researchers interested in undertaking research in this fiel

Análisis de la actividad científica de las universidades públicas españolas en el área de las tecnologías informáticas

The increasing competition among scientific organizations for limited resources requires researchers to publish quality papers, causing the development of tools to establish the most influential institutions. This bibliometric analysis characterizes research activity of Spanish universities and their academic staff in the area of computer sciences, identifying both their strengths and weaknesses nationwide. The analysis is also performed by autonomous regions, public universities, subject areas and professional standing. Thanks to this analysis a comprehensive overview of the current situation in the area of computer sciences is achieved.<br><br>La creciente competencia entre organismos científicos por los recursos limitados exige que los investigadores tengan que publicar con calidad y en cantidad. Ello ha provocado la aparición de herramientas a diferentes niveles para establecer qué instituciones son más influyentes en el mundo científico. Este análisis bibliométrico caracteriza la producción científica de las universidades españolas y sus profesores funcionarios en el área de las tecnologías informáticas, detectando tanto las fortalezas como las debilidades de los mismos a nivel nacional. Dicho análisis se realiza también por comunidades autónomas, universidades públicas, áreas de conocimiento y categoría profesional. Gracias a este análisis se consigue una visión global y detallada de la situación actual en el área de las tecnologías informáticas

Análisis de la actividad científica de las universidades públicas españolas en el área de las tecnologías informáticas

The increasing competition among scientific organizations for limited resources requires researchers to publish quality papers, causing the development of tools to establish the most influential institutions. This bibliometric analysis characterizes research activity of Spanish universities and their academic staff in the area of computer sciences, identifying both their strengths and weaknesses nationwide. The analysis is also performed by autonomous regions, public universities, subject areas and professional standing. Thanks to this analysis a comprehensive overview of the current situation in the area of computer sciences is achieved.La creciente competencia entre organismos científicos por los recursos limitados exige que los investigadores tengan que publicar con calidad y en cantidad. Ello ha provocado la aparición de herramientas a diferentes niveles para establecer qué instituciones son más influyentes en el mundo científico. Este análisis bibliométrico caracteriza la producción científica de las universidades españolas y sus profesores funcionarios en el área de las tecnologías informáticas, detectando tanto las fortalezas como las debilidades de los mismos a nivel nacional. Dicho análisis se realiza también por comunidades autónomas, universidades públicas, áreas de conocimiento y categoría profesional. Gracias a este análisis se consigue una visión global y detallada de la situación actual en el área de las tecnologías informáticas

The mf-index: A Citation-Based Multiple Factor Index to Evaluate and Compare the Output of Scientists

Comparing the output of scientists as objective as possible is an important factor for, e.g., the approval of research funds or the filling of open positions at universities. Numeric indices, which express the scientific output in the form of a concrete value, may not completely supersede an overall view of a researcher, but provide helpful indications for the assessment. This work introduces the most important citation-based indices, analyzes their advantages and disadvantages and provides an overview of the aspects considered by them. On this basis, we identify the criteria that an advanced index should fulfill, and develop a new index, the mf-index. The objective of the mf-index is to combine the benefits of the existing indices, while avoiding as far as possible their drawbacks and to consider additional aspects. Finally, an evaluation based on data of real publications and citations compares the mf-index with existing indices and verifies that its advantages in theory can also be determined in practice