An Authoritative Approach to Citation Classification

Pride, David and Knoth, Petr (2020). An Authoritative Approach to Citation Classification. In: ACM/IEEE Joint Conference on Digital Libraries in 2020 (JCDL ’20), 1-5 Aug 2020, Virtual - China.

DOI: https://doi.org/10.1145/3383583.3398617

URL: https://www.jcdl.org/

Abstract

The ability to understand not only that a piece of research has been cited, but why it has been cited has wide-ranging applications in the areas of research evaluation, in tracking the dissemination of new ideas and in better understanding research impact. There have been several studies that have collated datasets of citations anno- tated according to type using a class schema. These have favoured annotation by independent annotators and the datasets produced have been fairly small. We argue that authors themselves are in a primary position to answer the question of why something was cited. No previous study has, to our knowledge, undertaken such a large-scale survey of authors to ascertain their own personal rea- sons for citation. In this work, we introduce a new methodology for annotating citations and a significant new dataset of 11,233 citations annotated by 883 authors. This is the largest dataset of its type compiled to date, the first truly multi-disciplinary dataset and the only dataset annotated by authors. We also demonstrate the scalability of our data collection approach and perform a compari- son between this new dataset and those gathered by two previous studies.

Viewing alternatives

Metrics

Item Actions

Export

You can export this page using these formats Digital Object Identifier - DOI