Predicting long-term publication impact through a combination of early citations and journal impact factor

The ability to predict the long-term impact of a scientific article soon after its publication is of great value towards accurate assessment of research performance. In this work we test the hypothesis that good predictions of long-term citation counts can be obtained through a combination of a publication's early citations and the impact factor of the hosting journal. The test is performed on a corpus of 123,128 WoS publications authored by Italian scientists, using linear regression models. The average accuracy of the prediction is good for citation time windows above two years, decreases for lowly-cited publications, and varies across disciplines. As expected, the role of the impact factor in the combination becomes negligible after only two years from publication

Understanding the Impact of Early Citers on Long-Term Scientific Impact

This paper explores an interesting new dimension to the challenging problem of predicting long-term scientific impact (LTSI) usually measured by the number of citations accumulated by a paper in the long-term. It is well known that early citations (within 1-2 years after publication) acquired by a paper positively affects its LTSI. However, there is no work that investigates if the set of authors who bring in these early citations to a paper also affect its LTSI. In this paper, we demonstrate for the first time, the impact of these authors whom we call early citers (EC) on the LTSI of a paper. Note that this study of the complex dynamics of EC introduces a brand new paradigm in citation behavior analysis. Using a massive computer science bibliographic dataset we identify two distinct categories of EC - we call those authors who have high overall publication/citation count in the dataset as influential and the rest of the authors as non-influential. We investigate three characteristic properties of EC and present an extensive analysis of how each category correlates with LTSI in terms of these properties. In contrast to popular perception, we find that influential EC negatively affects LTSI possibly owing to attention stealing. To motivate this, we present several representative examples from the dataset. A closer inspection of the collaboration network reveals that this stealing effect is more profound if an EC is nearer to the authors of the paper being investigated. As an intuitive use case, we show that incorporating EC properties in the state-of-the-art supervised citation prediction models leads to high performance margins. At the closing, we present an online portal to visualize EC statistics along with the prediction results for a given query paper

Fuzzy Logic and Its Uses in Finance: A Systematic Review Exploring Its Potential to Deal with Banking Crises

The major success of fuzzy logic in the field of remote control opened the door to its application in many other fields, including finance. However, there has not been an updated and comprehensive literature review on the uses of fuzzy logic in the financial field. For that reason, this study attempts to critically examine fuzzy logic as an effective, useful method to be applied to financial research and, particularly, to the management of banking crises. The data sources were Web of Science and Scopus, followed by an assessment of the records according to pre-established criteria and an arrangement of the information in two main axes: financial markets and corporate finance. A major finding of this analysis is that fuzzy logic has not yet been used to address banking crises or as an alternative to ensure the resolvability of banks while minimizing the impact on the real economy. Therefore, we consider this article relevant for supervisory and regulatory bodies, as well as for banks and academic researchers, since it opens the door to several new research axes on banking crisis analyses using artificial intelligence techniques

Predicting the impact of academic articles on marketing research: Using machine learning to predict highly cited marketing articles

The citation count of an academic article is of great importance to researchers and readers. Due to the large increase in the publication of academic articles every year, it may be difficult to recognize the articles which are important to the field. This thesis collected data from Scopus with the purpose to analyze how paper, journal, and author related variables performed as drivers of article impact in the marketing field, and how well they could predict highly cited articles five years ahead in time. Social network analysis was used to find centrality metrics, and citation count one year after publication was included as the only time dependent variable. Our results found that citations after one year is a strong driver and predictor for future citations after five years. The analysis of the co-authorship network showed that closeness centrality and betweenness centrality are drivers of future citations in the marketing field, indicating that being close to the core of the network and having brokerage power is important in the field. With the use of machine learning methods, we found that a combination of paper, journal, and author related drivers perform better at predicting highly cited articles after five years, compared to using only one type of driver.nhhma

Predicting Citation Impact of Academic Papers Across Research Areas Using Multiple Models and Early Citations

As the volume of scientific literature expands rapidly, accurately gauging and predicting the citation impact of academic papers has become increasingly imperative. Citation counts serve as a widely adopted metric for this purpose. While numerous researchers have explored techniques for projecting papers' citation counts, a prevalent constraint lies in the utilization of a singular model across all papers within a dataset. This universal approach, suitable for small, homogeneous collections, proves less effective for large, heterogeneous collections spanning various research domains, thereby curtailing the practical utility of these methodologies. In this study, we propose a pioneering methodology that deploys multiple models tailored to distinct research domains and integrates early citation data. Our approach encompasses instance-based learning techniques to categorize papers into different research domains and distinct prediction models trained on early citation counts for papers within each domain. We assessed our methodology using two extensive datasets sourced from DBLP and arXiv. Our experimental findings affirm that the proposed classification methodology is both precise and efficient in classifying papers into research domains. Furthermore, the proposed prediction methodology, harnessing multiple domain-specific models and early citations, surpasses four state-of-the-art baseline methods in most instances, substantially enhancing the accuracy of citation impact predictions for diverse collections of academic papers

How to accomplish a highly cited paper in the tourism, leisure and hospitality field

This paper identifies the main factors that affect the citation rate of an article published in the tourism, leisure and hospitality field. Using several regression techniques, it has been identified that the number of references for an article, the reputation of the main author, and obtaining early citations have a major impact on a document’s citation rate. As well as this, by means of a quantitative–qualitative analysis (fsQCA), the most efficient combinations of factors that influence the number of citations received have also been identified. This paper is useful for researchers, editors and readers interested in improving the impact of their researchS