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Estimating Number of Citations Using Author Reputation

  • Carlos Castillo
  • Debora Donato
  • Aristides Gionis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4726)

Abstract

We study the problem of predicting the popularity of items in a dynamic environment in which authors post continuously new items and provide feedback on existing items. This problem can be applied to predict popularity of blog posts, rank photographs in a photo-sharing system, or predict the citations of a scientific article using author information and monitoring the items of interest for a short period of time after their creation. As a case study, we show how to estimate the number of citations for an academic paper using information about past articles written by the same author(s) of the paper. If we use only the citation information over a short period of time, we obtain a predicted value that has a correlation of r = 0.57 with the actual value. This is our baseline prediction. Our best-performing system can improve that prediction by adding features extracted from the past publishing history of its authors, increasing the correlation between the actual and the predicted values to r = 0.81.

Keywords

Link Prediction Average Citation Prediction Task Citation Information Citation Relationship 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Carlos Castillo
    • 1
  • Debora Donato
    • 1
  • Aristides Gionis
    • 1
  1. 1.Yahoo! Research Barcelona, C/Ocata 1, 08003 Barcelona, CatalunyaSpain

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