Predicting the citations of scholarly paper

A bibliometric approach to understanding the recent development of self-sufficient fish feed production utilizing agri-food wastes and by-products towards sustainable aquaculture

The global agri-food industry generates a large volume of waste annually, which causes both environmental and economic problems. Recently, there has been a growing interest in the use of agri-food wastes and by-products to produce self-sufficient fish feed. This study aimed to analyze the intellectual structure of the recent research on the utilization of agri-food wastes and by-products as self-sufficient fish feed materials based on 922 Scopus-indexed core collection documents from 252 journals written by 4420 authors from 73 countries with an annual growth rate of 18.65% over the last four years (2019-2022). This bibliometric study implemented knowledge domain visualization (KDV) using VOSViewer and Biblioshiny in the Bibliometrix R-package to investigate the basic scientometric profile of the selected fields. The results showed that Dawood M.A.O., with PageRanks of 0.0732, 19 total publications, 695 global citations from 2019 to 2022, and closeness values of 0.25, was the most productive author within the field. Subsequently, China was determined to be the most productive country (93 valid documents) and have the strongest collaboration network. Major research hotspots in the field included aquaculture and sustainable aquaculture, fish feed with agri-food waste, rainbow trout species, the development of a circular economy, probiotic applications, and cell signaling cytokines and peptides. This bibliometric study provides comprehensive information on the intellectual domain and research landscape on self-sufficient fish feed and also shows how interest in this research topic and similar ones is growing.

Hybrid Gold Open Access Citation Advantage in Clinical Medicine: Analysis of Hybrid Journals in the Web of Science

Biomedical fields have seen a remarkable increase in hybrid Gold open access articles. However, it is uncertain whether the hybrid Gold open access option contributes to a citation advantage, an increase in the citations of articles made immediately available as open access regardless of the article’s quality or whether it involves a trending topic of discussion. This study aimed to compare the citation counts of hybrid Gold open access articles to subscription articles published in hybrid journals. The study aimed to ascertain if hybrid Gold open access publications yield an advantage in terms of citations. This cross-sectional study included the list of hybrid journals under 59 categories in the ‘Clinical Medicine’ group from Clarivate’s Journal Citation Reports (JCR) during 2018–2021. The number of citable items with ‘Gold Open Access’ and ‘Subscription and Free to Read’ in each journal, as well as the number of citations of those citable items, were extracted from JCR. A hybrid Gold open access citation advantage was computed by dividing the number of citations per citable item with hybrid Gold open access by the number of citations per citable item with a subscription. A total of 498, 636, 1009, and 1328 hybrid journals in the 2018 JCR, 2019 JCR, 2020 JCR, and 2021 JCR, respectively, were included in this study. The citation advantage of hybrid Gold open access articles over subscription articles in 2018 was 1.45 (95% confidence interval (CI), 1.24–1.65); in 2019, it was 1.31 (95% CI, 1.20–1.41); in 2020, it was 1.30 (95% CI, 1.20–1.39); and in 2021, it was 1.31 (95% CI, 1.20–1.42). In the ‘Clinical Medicine’ discipline, the articles published in the hybrid journal as hybrid Gold open access had a greater number of citations when compared to those published as a subscription, self-archived, or otherwise openly accessible option.

A bibliometric analysis and visualization of e-learning adoption using VOSviewer

Even though being perceived as a novel approach, multiple authors claim that the digital transition of all sectors in society started when information and communication technologies (ICT) started to be an integral part of our daily lives. The education sector currently represents one of the contexts where the use of ICT is more promising and allows to reach greater benefits, mostly due to the wide range of tools, applications, and management and methodological approaches that are associated with e-learning. With the above in mind, a bibliometric analysis of the e-learning adoption topic has been performed, aiming on delivering a detailed analysis of the status of the topic. This analysis was carried out by analyzing the scientific literature indexed in the Scopus database that addressed the multiple stages of the e-learning adoption process (i.e., acceptance, adoption, and use). Our study analyzed 896 documents published between 1989 and 2021, of which 98.3% represented papers published in journals and conference proceedings.

Predictive analysis of multiple future scientific impacts by embedding a heterogeneous network

Identifying promising research as early as possible is vital to determine which research deserves investment. Additionally, developing a technology for automatically predicting future research trends is necessary because of increasing digital publications and research fragmentation. In previous studies, many researchers have performed the prediction of scientific indices using specially designed features for each index. However, this does not capture real research trends. It is necessary to develop a more integrated method to capture actual research trends from various directions. Recent deep learning technology integrates different individual models and makes it easier to construct more general-purpose models. The purpose of this paper is to show the possibility of integrating multiple prediction models for scientific indices by network-based representation learning. This paper will conduct predictive analysis of multiple future scientific impacts by embedding a heterogeneous network and showing that a network embedding method is a promising tool for capturing and expressing scientific trends. Experimental results show that the multiple heterogeneous network embedding improved 1.6 points than a single citation network embedding. Experimental results show better results than baseline for the number of indices, including the author h-index, the journal impact factor (JIF), and the Nature Index after three years from publication. These results suggest that distributed representations of a heterogeneous network for scientific papers are the basis for the automatic prediction of scientific trends.

Suicide and suicidal ideation in the times of the Covid-19 pandemic: A scientometric analysis

OBJECTIVES

The article aims to explore the studies performed on suicide because of coronavirus disease 19 through a bibliometric analysis. A quantitative analysis of the topic furnishes data on the publication pattern, influential research journals, highly cited articles, productive countries and organizations, the authorship pattern, and the collaborative pattern between authors.

METHODS

Data regarding the type of documents, most cited articles, influential research journals, contributions per country, and so on were extracted for the study from the Scopus database. Data analysis and visualization were performed through R-Studio and the VOSviewer application.

RESULTS

A bibliometric analysis encompassing scientific contributions based on suicide or suicide-related ideation because of the coronavirus pandemic showed a total of 494 documents published in 230 journals/books. The articles published by proficient authors in reputed journals highlighted the key areas of research in the field. USA dominated the list of scientific production of countries contributing to 340 documents.

CONCLUSION

The results provided by this analysis could act as a steppingstone for experts to design a roadmap for mental health research during the pandemic. Studies can be designed to gather information on mental health conditions across specific age groups. Research collaborations that facilitate the publication of pooled protocols and data are encouraged.

A modified LSTM network to predict the citation counts of papers

Quantifiable predictability in the citation counts of articles is significant in scientometrics and informetrics. Many metrics based on the citation counts can evaluate the scientific impact of research articles and journals. Utilising time series models, an article’s citation counts up to the yth year after publication can be predicted by those up to the previous years. However, the typically used models cannot predict the fat tail of the actual citation distributions. Thus, based on cumulative advantage of the citation behaviour, we propose a method to predict the accumulated citation counts, by using a random number sampled from a power-law distribution to modify the results given by a recurrent neural network (RNN), long short-term memory. Extensive experiments on the data set including 17 journals in information science verified the effectiveness of our method by the good fittings on distributions and evolutionary trends of the citation counts of articles. Our method has the potential to be extended to predict other popular assessment measures such as impact factor and h-index for journals.

Predicting article citations using data from 100 top-cited publications in the field of Psoriasis Vulgaris and biological agents (PVBA) since 1991: A bibliometric analysis

BACKGROUND

Psoriasis Vulgaris is a chronic inflammatory disease characterized by keratinocyte hyperproliferation. Bibliometric analysis helps determine the most influential article on the topic of "Psoriasis Vulgaris and biological agents (PVBAs)", and what factors affect article citation remain unclear. This study aims (1) to identify the top 100 most cited articles in PVBA (PVBA100 for short) from 1991 to 2020, (2) to visualize dominant entities on one diagram using data in PVBA100, and (3) to investigate whether medical subject headings (MeSH terms) can be used to predict article citations.

METHODS

The top 100 most cited articles relevant to PVBA (1991-2020) were downloaded by searching the PubMed database. Citation analysis was applied to compare the dominant roles in article types and topic categories using pyramid plots. Social network analysis (SNA) and Sankey diagrams were applied to highlight prominent entities. We examined the MeSH prediction effect on article citations using its correlation coefficients.

RESULTS

The most frequent article types and topic categories were research support by institutes (46%) and drug therapy (88%), respectively. The most productive countries were the United States (38%), followed by Germany (13%) and Japan (12%). Most articles were published in Br J Dermatol (13%) and J Invest Dermatol (11%). MeSH terms were evident in the prediction power of the number of article citations (correlation coefficient=0.45, t=4.99).

CONCLUSIONS

The breakthrough was made by developing one dashboard to display PVBA100. MeSH terms can be used for predicting article citations in PVBA100. These visualizations of PVBA100 could be applied to future academic pursuits and applications in other academic disciplines.

A Machine Learning Model to Predict Citation Counts of Scientific Papers in Otology Field

One of the most widely used measures of scientific impact is the number of citations. However, due to its heavy-tailed distribution, citations are fundamentally difficult to predict but can be improved. This study was aimed at investigating the factors and parts influencing the citation number of a scientific paper in the otology field. Therefore, this work proposes a new solution that utilizes machine learning and natural language processing to process English text and provides a paper citation as the predicted results. Different algorithms are implemented in this solution, such as linear regression, boosted decision tree, decision forest, and neural networks. The application of neural network regression revealed that papers' abstracts have more influence on the citation numbers of otological articles. This new solution has been developed in visual programming using Microsoft Azure machine learning at the back end and Programming Without Coding Technology at the front end. We recommend using machine learning models to improve the abstracts of research articles to get more citations.

COVID-19 citation pandemic within the psychological knowledge domain

Presented study explores the knowledge domain of psychological research published in 2020 and 2021. Metadata from 156,942 psychology papers available in Scopus were analyzed using citation analysis and bibliographic mapping techniques. Having in mind the ubiquity of the COVID-19 pandemic and the numerous ways it has affected people's lives, the fact that COVID-19-related papers represent only 2% to 7% of the total academic production in psychology may seem rather low. However, these papers have attracted much more attention from the public than non-COVID papers. They were also cited two to eight times more frequently, depending on the measure used, and account for 16% to 19% of total citations to psychology papers. Results show that early-stage researchers and those who had fewer articles in Scopus have benefited more from publishing COVID papers. They have managed to boost their average citation rates to the level of their colleagues who were much longer active and previously had higher citedness. Results indicate that the authors citing behavior largely follows the cumulative advantage pattern. Psychological research in general is mainly focused on mental health, anxiety, depression, and stress. This trend is even more fostered due to the pandemic since some of these topics are often analyzed as typical emotional reactions to COVID-19. Other relevant issues are also very well covered, except for the question how scientific results are communicated to the public. The role of "hot" papers was elaborated from the perspective of research evaluation practice.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12144-022-03146-3.

The Effect of Article Characteristics on Citation Number in a Diachronic Dataset of the Biomedical Literature on Chronic Inflammation: An Analysis by Ensemble Machines

Citations are core metrics to gauge the relevance of scientific literature. Identifying features that can predict a high citation count is therefore of primary importance. For the present study, we generated a dataset of 121,640 publications on chronic inflammation from the Scopus database, containing data such as titles, authors, journal, publication date, type of document, type of access and citation count, ranging from 1951 to 2021. Hence we further computed title length, author count, title sentiment score, number of colons, semicolons and question marks in the title and we used these data as predictors in Gradient boosting, Bagging and Random Forest regressors and classifiers. Based on these data, we were able to train these machines, and Gradient Boosting achieved an F1 score of 0.552 on classification. These models agreed that document type, access type and number of authors were the best predicting factors, followed by title length.

Evaluating the impact of citations of articles based on knowledge flow patterns hidden in the citations

The effective evaluation of the impact of a scholarly article is a significant endeavor; for this reason, it has garnered attention. From the perspective of knowledge flow, this paper extracted various knowledge flow patterns concealed in articles citation counts to describe the citation impact of the articles. First, the intensity characteristic of knowledge flow was investigated to distinguish the different citation vitality of articles. Second, the knowledge diffusion capacity was examined to differentiate the size of the scope of articles’ influences on the academic environment. Finally, the knowledge transfer capacity was discussed to investigate the support degree of articles on the follow-up research. Experimental results show that articles got more citations recently have a higher knowledge flow intensity. The articles have various impacts on the academic environment and have different supporting effects on the follow-up research, representing the differences in their knowledge diffusion and knowledge transfer capabilities. Compared with the single quantitative index of citation frequency, these knowledge flow patterns can carefully explore the citation value of articles. By integrating the three knowledge flow patterns to examine the total citation impact of articles, we found that the articles exhibit distinct value of citation impact even if they were published in the same field, in the same year, and with similar citation frequencies.