Browsing by Author "Solanki Gupta"

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Altmetric data quality analysis using Benford’s law
(Akademiai Kiado ZRt., 2024) Solanki Gupta; Vivek Kumar Singh; Sumit Kumar Banshal
Altmetrics, or alternative metrics, refer to the newer kind of events around scholarly articles, such as the number of times the article is read, tweeted, mentioned in blog posts etc. These metrics have gained a lot of popularity during last few years and are now being collected and used in several ways, ranging from early measure of article impact to a potential indicator of societal relevance of research. However, there are several studies which have cautioned about use of altmetrics on account of quality and reliability of altmetric data, as they may be more prone to manipulations and artificial inflations. This study proposes a framework based on application of Benford’s Law to evaluate the quality of altmetric data. A large sized altmetric data sample is considered and the fits with Benford’s Law are computed. The analysis is performed by doing plots of the empirical data distributions and the theoretical Benford's, and by employing relevant statistical measures and tests. Results for fit on first and second leading digit of altmetric data show conformity to Benford's distribution. To further explore the usefulness of the framework, the altmetric data is subjected to artificial manipulations through a systematic process and the fits to Benford’s law are reassessed to see if there are distortions. The results and analysis suggest that Benford’s Law based framework can be used to test the quality of altmetric data. Relevant implications of the research are discussed. © Akadémiai Kiadó, Budapest, Hungary 2024.
An empirical analysis of existence of power laws in social media mentions to scholarly articles
(International Society for Scientometrics and Informetrics, 2021) Sumit Kumar Banshal; Aparna Basu; Vivek Kumar Singh; Hiran H. Lathabai; Solanki Gupta; Pranab K. Muhuri
Power laws are a characteristic distribution found in both natural as well as in man-made systems. Previous studies have shown that citations to scientific articles follow a power law, i.e., the number of papers having a certain level of citation x are proportional to x raised to some negative power. However, the distributional character of altmetrics (such as reads, likes, mentions, etc.) has not been studied in much detail, particularly with respect to existence of power law behaviours. This article, therefore, attempts to do an empirical analysis of altmetric mention data of a large set of scholarly articles to see if they exhibit power law. The individual and the composite data series of 'mentions' on the various platforms are fit to a power law distribution, and the parameters and goodness of fit determined using least squares regression. We also explore fit to other distributions like the log-normal and Hooked Power Law. Results obtained confirm the existence of power law behaviour in social media mentions to scholarly articles and we conclude that altmetric distributions also follow power law with a fairly good fit over a wide range of values. © 2021 18th International Conference on Scientometrics and Informetrics, ISSI 2021. All rights reserved.
Distributional characteristics of Dimensions concepts: An Empirical Analysis using Zipf’s law
(Akademiai Kiado ZRt., 2024) Solanki Gupta; Vivek Kumar Singh
The massive growth in scholarly outputs during the last few decades has resulted into the creation of several scholarly databases to index the outputs. These scholarly databases index publication records and provide different metadata fields for different kinds of usage ranging from retrieval and research evaluation to various scientometric analysis. The ‘author keywords’ is one such important metadata field provided by many databases and used for different text-based and thematic structure analysis. The Dimensions database, however, does not provide ‘author keywords’ metadata field, instead it provides automatically generated terms from the article full texts, called ‘concepts’. Therefore, it is not clear whether different text-based analysis can be done with data provided by Dimensions database. Therefore, this article explores the distributional characteristics of Dimensions concepts. The Dimensions concept data obtained for a sufficiently large sample of scholarly articles is analysed through rank frequency distribution plots in the log–log space. Existence of Zipfian distribution is explored. The results indicate that Dimensions concepts adhere to the Zipfian properties which in turn indicates that Dimensions concepts have similar distributional characteristics as author keywords and hence they may have the same expressive power as that of author or index keywords for scientometric exercises. The study is novel as it is the first study to explore the distributional characteristics of the Dimensions concepts, particularly with respect to Zipfian properties, which provide the statistical foundation for understanding the Dimensions concepts and help to model and analyse them. © Akadémiai Kiadó, Budapest, Hungary 2024.
Patterns in the Growth and Thematic Evolution of Artificial Intelligence Research: A Study Using Bradford Distribution of Productivity and Path Analysis
(Wiley-Hindawi, 2024) Solanki Gupta; Anurag Kanaujia; Hiran H. Lathabai; Vivek Kumar Singh; Philipp Mayr
Artificial intelligence (AI) has emerged as a transformative technology with applications across multiple domains. The corpus of work related to the field of AI has grown significantly in volume as well as in terms of the application of AI in wider domains. However, given the wide application of AI in diverse areas, the measurement and characterization of the span of AI research is often a challenging task. Bibliometrics is a well-established method in the scientific community to measure the patterns and impact of research. It however has also received significant criticism for its overemphasis on the macroscopic picture and the inability to provide a deep understanding of growth and thematic structure of knowledge-creation activities. Therefore, this study presents a framework comprising of two techniques, namely, Bradford's distribution and path analysis to characterize the growth and thematic evolution of the discipline. While the Bradford distribution provides a macroscopic view of artificial intelligence research in terms of patterns of growth, the path analysis method presents a microscopic analysis of the thematic evolutionary trajectories, thereby completing the analytical framework. Detailed insights into the evolution of each subdomain are drawn, major techniques employed in various AI applications are identified, and some relevant implications are discussed to demonstrate the usefulness of the analyses. © 2024 Solanki Gupta et al.
Power Laws in altmetrics: An empirical analysis
(Elsevier Ltd, 2022) Sumit Kumar Banshal; Solanki Gupta; Hiran H. Lathabai; Vivek Kumar Singh
Power Laws are a characteristic distribution found in both natural as well as in man-made systems. Previous studies have shown that citations to scientific articles follow a power law, i.e., the number of papers having a certain level of citation x are proportional to x raised to some negative power. However, the distributional character of altmetrics (such as reads, likes, mentions, etc.) has not been studied in much detail, particularly with respect to existence of power law behaviours. This article, therefore, attempts to do an empirical analysis of altmetric mention data of a large set of scholarly articles to see if they exhibit power law. The individual and the composite data series of 'mentions' on the various platforms are fit to a power law distribution, and the parameters and goodness of fit are determined, both using least squares regression as well as the Maximum Likelihood Estimate (MLE) approach. We also explore the fit of the mention data to other distribution families like the Log-normal and exponential distributions. Results obtained confirm the existence of power law behaviour in social media mentions to scholarly articles. The Log-normal distribution also looks plausible but is not found to be statistically significant, and the exponential distribution does not show a good fit. Major implications of power law in altmetrics are given and interesting research questions are posed in pursuit of enhancing the reliability of altmetrics for research evaluation purposes. © 2022 Elsevier Ltd. All rights reserved.
Quantitative Estimation of Trends in Artificial Intelligence Research: A Study of Bradford Distributions using Leimkuhler Model
(Phcog.Net, 2023) Solanki Gupta; Vivek Kumar Singh
The ubiquitous applications of Artificial Intelligence (AI) in various domains of human life have resulted in a phenomenal increase in AI research. The research output in AI has grown rapidly during the last decade. While some scientometric studies have noted this growth in publications, there are virtually no studies that could characterize the growth in publications in terms of the increase in domains and journals in which AI research is being carried out and published. This article makes an attempt to fill this research gap by using the Leimkuhler model of Bradford’s law of productivity to produce quantitative estimates of AI research publishing. Publications indexed in Web of Science for the period 2011 to 2020 are used for analysis. The analysis explains the variation in the corpus of AI research using productivity distribution and its characteristics. The quantitative findings support the idea that AI research has not only increased in volume but also in terms of applications to a wider list of areas. Copyright Author (s) 2023 Distributed under.
Tracing scientific and technological development in genetically modified crops
(Springer Science and Business Media Deutschland GmbH, 2024) Anurag Kanaujia; Solanki Gupta
Genetically Modified (GM) Organisms have been used in various domains since their introduction in the 1980s. According to ISAAA data, the use of GM crops in agriculture has also increased significantly in the past 30 years. However, even after 3 decades of commercialisation, GM crops are still surrounded with controversies with different countries adopting varying approaches to their introduction in the consumer markets, owing to different stances of various stakeholders. Motivated by this multitude of opinions, and absence of knowledge mapping, this study has undertaken scientometric analysis of the publication (Web of Science) and patent (Lens.org) data about genetically modified technology use in agriculture to explore the changing knowledge patterns and technological advancements in the area. It explores both scientific and technological perspectives regarding the use of Genetically Modified Crops, by using publication as well as patent data. The findings of this study highlight the major domains of research, technology development, and leading actors in the ecosystem. These findings can be helpful in taking effective policy decisions, and furthering the research activities. It presents a composite picture using both publications and patent data. Further, it will be of utility to explore the other technologies which are replacing GM technology in agriculture in future studies. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.