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.
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.
- Award ID(s):
- 1912898
- Publication Date:
- NSF-PAR ID:
- 10095438
- Journal Name:
- Information Systems Frontiers
- Page Range or eLocation-ID:
- 1-14
- ISSN:
- 1387-3326
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1007/s10796-018-9889-9
