Modeling citation dynamics of “atypical” articles

Abstract

Modeling and predicting citation dynamics of individual papers is important due to its critical role in a wide range of decisions in science. While current modeling framework successfully captures citation dynamics of typical papers, there exists a non-negligible, and perhaps most interesting, fraction of atypical papers whose citation trajectories do not follow the normal rise-andfall pattern. Here, we systematically study and classify citation patterns of atypical papers, finding that they can be characterized by sleeping beauties, second acts, and a combination of both. We propose a second-act model that can accurately describe the citation dynamics of second-act papers. The model not only provides a mechanistic framework to understand citation patterns of atypical papers, separating factors that drive impact, but it also offers new capabilities to identify the time of exogenous events that influence citations.

Sleeping beauties gain impact in overdrive mode

“…This phenomenon is also referred to as cumulative advantage. He et al (2018) found that the WSB model cannot fully explain the observed citation patterns of 'second act' papers and they developed a new model. However, these models cannot predict the citation dynamics of papers where typical 'exogenous' events cause a sudden boost of citations, which is the case in our definition of SBs.…”

“…Li and Shi (2016) studied the awakening of SBs in 'genius work', i.e., scientific articles of which the citations grow exponentially in a very long period, for example, 50 years. He et al (2018) studied atypical articles whose citation trajectories do not follow the normal rise-and-fall pattern. Ye and Bornmann (2018) and Bornmann et al (2018) investigated the differences between papers that received many citations shortly after publication and SBs.…”

Can we ‘feel’ the temperature of knowledge? Modelling scientific popularity dynamics via thermodynamics

“…Citation-based analysis always occupies a predominant role for impact assessment because of the quantitative characteristics of citations and more importantly, the positive correlation between citation and scientific influence [7,8]. For an article, citation dynamics reveals its temporal evolution of impact [9][10][11] and popularity [12]. For a researcher, the evolution of individual citation statistics portraits his or her activity [13], scholar impact dynamics [14][15][16] and research interest pattern [17].…”

As Relações Entre Variáveis Que Afetam O Desempenho De Uma Startup: Um Estudo Bibliométrico E Sociométrico

“…Para isto, foi utilizado o método de análise da rede de citações das publicações. (He, Lei & Wang, 2018). Assim, embora existam alguns problemas com o uso de dados de citações -por exemplo, citação negativa (isto é, citação que levanta dúvidas sobre a veracidade do que é dito na obra citada), autocitação e ausência de citação, a análise da rede de citações pode ser usada para avaliar o nível de contribuição que uma publicação traz para o avanço do conhecimento científico (Flatt, Blasimme & Vayena, 2017).…”