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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.

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URL: https://www.jcdl.org/
DOI (Digital Object Identifier) Link: https://doi.org/10.1145/3383583.3398617
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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.

Item Type: Conference or Workshop Item
Copyright Holders: 2020 ACM
Project Funding Details:
Funded Project NameProject IDFunding Body
OpenMinTeD654021EC (European Commission): FP(inc.Horizon2020, H2020, ERC)
JIsc Digital Services - CORENot SetJISC (Joint Information Systems Committee)
Keywords: Citation typing; citation classification; data mining; Open Access; scholarly data; research evaluation
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Big Scientific Data and Text Analytics Group (BSDTAG)
Item ID: 70520
Depositing User: David Pride
Date Deposited: 18 May 2020 14:06
Last Modified: 21 May 2020 16:16
URI: http://oro.open.ac.uk/id/eprint/70520
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