Citations have long been used to characterize
the state of a scientific field and to identify
influential works. However, writers use
citations for different purposes, and this varied
purpose influences uptake by future scholars.
Unfortunately, our understanding of how
scholars use and frame citations has been limited
to small-scale manual citation analysis of
individual papers. We perform the largest behavioral
study of citations to date, analyzing
how scientific works frame their contributions
through different types of citations and how
this framing affects the field as a whole. We
introduce a new dataset of nearly 2,000 citations
annotated for their function, and use it
to develop a state-of-the-art classifier and label
the papers of an entire field: Natural Language
Processing. We then show how differences
in framing affect scientific uptake and
reveal the evolution of the publication venues
and the field as a whole. We demonstrate that
authors are sensitive to discourse structure and
publication venue when citing, and that how a
paper frames its work through citations is predictive
of the citation count it will receive. Finally,
we use changes in citation framing to
show that the field of NLP is undergoing a significant
increase in consensus.
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Is Self-Citation Biased? An Investigation via the Lens of Citation Polarity, Density, and Location
Traditional citation analysis methods have been criticized because their theoretical base of statistical counts does not reflect the motive or judgment of citing authors. In particular, self-citations may give undue credits to a cited article or mislead scientific development. This research aims to answer the question of whether self-citation is biased by probing into the motives and context of citations. It takes an integrated and fine-grained view of self-citations by examining them via multiple lenses—polarity, density, and location of citations. In addition, it explores potential moderating effects of citation level and associations among location contexts of citations to the same references for the first time. We analyzed academic publications across different topics and disciplines using both qualitative and quantitative methods. The results provide evidence that self-citations are free of bias in terms of citation density and polarity uncertainty, but they can be biased with respect to positivity and negativity of citations. Furthermore, this study reveals impacts of self-citing behavior on some citation patterns involving citation density, location concentration, and associations. The examination of self-citing behavior from those new perspectives shed new lights on the nature and function of self-citing behavior.
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- Award ID(s):
- 1912898
- NSF-PAR ID:
- 10095438
- Date Published:
- Journal Name:
- Information Systems Frontiers
- ISSN:
- 1387-3326
- Page Range / eLocation ID:
- 1-14
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s10796-018-9889-9
