Incidental or Influential? - Challenges in Automatically Detecting Citation Importance Using Publication Full Texts

Pride, David and Knoth, Petr (2017). Incidental or Influential? - Challenges in Automatically Detecting Citation Importance Using Publication Full Texts. In: Research and Advanced Technology for Digital Libraries (Kamps, Jaap; Tsakonas, Giannis; Manolopoulos, Yannis; Iliadis, Lazaros and Karydis, Ioannis eds.), Lecture Notes in Computer Science ; Information Systems and Applications, incl. Internet/Web, and HCI, Springer, Cham, Switzerland, pp. 572–578.

DOI: https://doi.org/10.1007/978-3-319-67008-9_48

Abstract

This work looks in depth at several studies that have attempted to automate the process of citation importance classification based on the publications’ full text. We analyse a range of features that have been previously used in this task. Our experimental results confirm that the number of in-text references are highly predictive of influence. Contrary to the work of Valenzuela et al. (2015), we find abstract similarity one of the most predictive features. Overall, we show that many of the features previously described in literature are not particularly predictive. Consequently, we discuss challenges and potential improvements in the classification pipeline, provide a critical review of the performance of individual features and address the importance of constructing a large scale gold-standard reference dataset.

Viewing alternatives

Metrics

Public Attention

Altmetrics from Altmetric

Number of Citations

Citations from Dimensions

Item Actions

Export

About

Recommendations