High-ranked social science journal articles can be identified from early citation information

Do citations accumulate too slowly in the social sciences to be used to assess the quality of recent articles? I investigate whether this is the case using citation data for all articles in economics and political science published in 2006 and indexed in the Web of Science. I find that citations in the first two years after publication explain more than half of the variation in cumulative citations received over a longer period. Journal impact factors improve the correlation between the predicted and actual future ranks of journal articles when using citation data from 2006 alone but the effect declines sharply thereafter. Finally, more than half of the papers in the top 20% in 2012 were already in the top 20% in the year of publication (2006)

Search for Evergreens in Science: A Functional Data Analysis

Evergreens in science are papers that display a continual rise in annual citations without decline, at least within a sufficiently long time period. Aiming to better understand evergreens in particular and patterns of citation trajectory in general, this paper develops a functional data analysis method to cluster citation trajectories of a sample of 1699 research papers published in 1980 in the American Physical Society (APS) journals. We propose a functional Poisson regression model for individual papers' citation trajectories, and fit the model to the observed 30-year citations of individual papers by functional principal component analysis and maximum likelihood estimation. Based on the estimated paper-specific coefficients, we apply the K-means clustering algorithm to cluster papers into different groups, for uncovering general types of citation trajectories. The result demonstrates the existence of an evergreen cluster of papers that do not exhibit any decline in annual citations over 30 years.Comment: 40 pages, 9 figure

Network Analysis for Predicting Academic Impact

How are scholars ranked for promotion, tenure and honors? How can we improve the quantitative tools available for decision makers when making such decisions? Current academic decisions are mostly very subjective. In the era of “Big Data,” a solid quantitative set of measurements should be used to support this decision process. This paper presents a method for predicting the probability of a paper being in the most cited papers using only data available at the time of publication. We find that structural network properties are associated with increased odds of being in the top percentile of citation count. The paper also presents a method for predicting the future impact of researchers, using information available early in their careers. This model integrates information about changes in a young researcher’s role in the citation network and co-authorship network and demonstrates how this improves predictions of their future impact

Weighted citation: An indicator of an article's prestige

We propose using the technique of weighted citation to measure an article's prestige. The technique allocates a different weight to each reference by taking into account the impact of citing journals and citation time intervals. Weighted citation captures prestige, whereas citation counts capture popularity. We compare the value variances for popularity and prestige for articles published in the Journal of the American Society for Information Science and Technology from 1998 to 2007, and find that the majority have comparable status.Comment: 17 pages, 6 figure

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