Quantifying Long-Term Scientific Impact

By Dashun Wang, Chaoming Song, Albert-László Barabási. Science, 2013

474 citations recorded by the source corpus.

Citation Grabbers Is there quantifiable regularity and predictability in citation patterns? It is clear that papers that have been cited frequently tend to accumulate more citations. It is also clear that, with time, even the most novel paper loses its currency. Some papers, however, seem to have an inherent “fitness” that can be interpreted as a community's response to the research. Wang et al. (p. 127; see the Perspective by Evans) developed a mechanistic model to predict citation history. The model links a paper's ultimate impact, represented by the total number of citations the paper will ever receive, to a single measurable parameter inferred from its early citation history. The model was used to identify factors that influence a journal's impact factor. Early citation history can be used to model the total number of citations a paper will receive and to compare journals. [Also see Perspective by Evans] The lack of predictability of citation-based measures frequently used to gauge impact, from impact factors to short-term citations, raises a fundamental question: Is there long-term predictability in citation patterns? Here, we derive a mechanistic model for the citation dynamics of individual papers, allowing us to collapse the citation histories of papers from different journals and disciplines into a single curve, indicating that all papers tend to follow the same universal temporal pattern. The observed patterns not only help us uncover basic mechanisms that govern scientific impact but also offer reliable measures of influence that may have potential policy implications.

Subfields: Management Science and Operations Research, Sociology and Political Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/dc0c75ad6801eda216505d492491fe15e500891e

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Citations in the corpus

The science of science: from the perspective of complex systems (2017, methodology): “[32]; a detailed description of the method appears in Section 4.”
Quantifying herding effects in crowd wisdom (2014, background): “For example, recent work has shown that the temporal dynamics of collective response to a publication follows rather reproducible patterns, as citations can be captured by a mechanistic model [23].”
Relative Citation Ratio (RCR): A New Metric That Uses Citation Rates to Measure Influence at the Article Level (2015, background): “This is because the accrual of citations follows a power law or log-normal pattern, in accordance with a process called preferential attachment [27,33,34].”
Defining and identifying Sleeping Beauties in science (2015, background): “Several factors can potentially affect the amount of citations accumulated by a paper over time, including its quality, timeliness, and potential to trigger further inquiries (9), the reputation of its authors (16, 17), as well as its topic and age (8).”
Modeling and Predicting Popularity Dynamics via Reinforced Poisson Processes (2014, methodology): “Existing studies on popularity dynamics include inﬂuence spread [13], trust propagation [11], information access pattern [9], group formation [1], culture market [21], popularity prediction of online content [8, 22], Web users’ behavior [15], and citation prediction [26, 28].”