Attention decay in science

@article{Parolo2015AttentionDI,
  title={Attention decay in science},
  author={Pietro Della Briotta Parolo and Raj Kumar Pan and Rumi Ghosh and Bernardo A. Huberman and Kimmo K. Kaski and Santo Fortunato},
  journal={J. Informetrics},
  year={2015},
  volume={9},
  pages={734-745}
}
The exponential growth in the number of scientific papers makes it increasingly difficult for researchers to keep track of all the publications relevant to their work. [...] Key Result This indicates that the attention of scholars depends on the number of published items, and not on real time.Expand
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119 Citations
Highly Influential Citations
7
Background Citations
44
Methods Citations
5
Results Citations
5

119 Citations

Citation Type

Citation Type

Unraveling the dynamics of growth, aging and inflation for citations to scientific articles from specific research fields
TLDR
Comparison with analogous results for the citation dynamics of technology-disaggregated cohorts of patents provides deeper insight into the basic principles of information propagation as indicated by citing behavior.
Understanding the Impact of Early Citers on Long-Term Scientific Impact
TLDR
It is found that influential EC negatively affects LTSI possibly owing to attention stealing, and it is shown that incorporating EC properties in the state-of-the-art supervised citation prediction models leads to high performance margins.
Properties of an index of citation durability of an article
TLDR
A seemingly contradictory result is found that more highly cited articles tend to have higher citation durability in individual journals while high-impact journals tend to include more articles with lower Citation durability in higher proportions.
Methods to account for citation inflation in research evaluation
TLDR
The study points to the need for a systemic overhaul of the counting methods used evaluating citation impact – especially in the case of researchers, journals, and institutions – which can span several decades and thus several doubling periods.
The Memory of Science: Inflation, Myopia, and the Knowledge Network
TLDR
A generative model of the citation network is developed, featuring exponential growth, the redirection of scientific attention via publications’ reference lists, and the crowding out of old literature by the new.
The time dimension of science: Connecting the past to the future
TLDR
A new theoretical framework is developed that allows to connect the two approaches to measuring citation age distributions through precise mathematical relationships, and helps to reconcile the interplay between temporal decay of citations and the growth of science, helping to uncover new functional forms characterizing citation age distribution.
Diversity of temporal influence in popularity prediction of scientific publications
TLDR
This paper introduced the diversity of temporal decay rate of the influence of citations to predict the future popularity of papers, and proposed a diverse temporal decay method that can improve the prediction accuracy compared with other popularity-based prediction methods.
Identification of milestone papers through time-balanced network centrality
TLDR
It is found that network-based metrics identify the Milestone Letters better than metrics based on the citation count, which suggests that the structure of the citation network contains information that can be used to improve the ranking of scientific publications.
Going beneath the shoulders of giants: tracking the cumulative knowledge spreading in a comprehensive citation network
TLDR
Analysis of cumulative knowledge creation on a global scale can be useful in estimating the type and scale of scientific influence of individual publications and entire research areas as well as yielding insights which could not be discovered by using only the direct citation counts.
Stochastic Block Model Reveals the Map of Citation Patterns and Their Evolution in Time
TLDR
This study maps out the large-scale structure of citation networks of science journals and follows their evolution in time by using stochastic block models (SBMs), and illustrates how these block networks can be used as maps of science.
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