Cited by 234scientometrics and bibliometrics researchWeb visibility and informetricsAcademic Writing and Publishing

The DOI matches its Crossref record; 91 of the 91 references it cites were found in a registry or an index; 4 of the 36 sources its claims cite were checked for support.

Measuring the Evolution of a Scientific Field through Citation Frames

Open access
Jurgens, David, Kumar, Srijan, Hoover, Raine, McFarland, Dan, Jurafsky, Dan·2018·Transactions of the Association for Computational Linguistics

Abstract

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.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

The paper introduces a six-class citation-function scheme and a classifier to label 134,127 citations across 21,474 NLP papers, showing citation framing varies by section and venue and predicts future impact. It argues NLP has shifted toward rapid discovery science, with declining comparison/contrast citations and increasing concentration on shared methods.

Key findings

  • Citation framing follows paper structure: BACKGROUND citations dominate introductions and related work, USES citations rise in methodology/evaluation, COMPARISON OR CONTRAST increases in results/discussion, and FUTURE citations are most frequent in conclusions.
  • Venue matters: journals have the highest percentage of BACKGROUND citations, conferences devote proportionally more to comparison/contrast, and workshops use more BACKGROUND with relatively little comparison; SemEval workshops are primarily USES citations.
  • A paper’s citation framing significantly improves prediction of its 5-year citation count: adding framing lowers AIC from 35,693.400 to 35,567.630 (p ≤ 0.01) on 10,434 papers; USES (0.055*), COMPARISON OR CONTRAST (0.025*), EXTENDS (0.021), MOTIVATION (0.014*), and BACKGROUND (0.013*) are positive predictors, while FUTURE is not significant.
  • NLP shows increased consensus: comparison/contrast citations decline (r = −0.899, p ≤ 0.01), non-methodological citations decline (r = −0.663, p ≤ 0.01), and incoming citations concentrate on the same compared works (r = 0.734, p ≤ 0.01) and used works (r = 0.889, p ≤ 0.01); workshops also become more conference-like over time, especially long-running workshops.

Methods

  • Built a six-class citation-function scheme (BACKGROUND, MOTIVATION, USES, EXTENSION, COMPARISON OR CONTRAST, FUTURE) and annotated 1,969 citations: 1,436 from 52 fully labeled papers (mean 27.6 citations/paper) plus 533 supplemental contexts from 133 papers; combined with Teufel’s data to create 3,083 instances.
  • Trained a Random Forest classifier using structural, lexical/grammatical, field, usage, topic-model, bootstrapped-pattern, and selectional-preference features; evaluated with paper-level cross-validation and macro-averaged F1; applied it to 21,474 ACL Anthology papers, yielding 134,127 citations.
  • Used negative binomial regression to predict cumulative 5-year citation counts for 10,434 papers, and Jensen-Shannon divergence to compare venue citation framing over time.

Limitations

  • The paper states errors remain; a main challenge is incorporating information external to the citing sentence, and citation context identification is important for improving performance.
  • The paper states some instances were excluded due to malformed context, ParsCit wrong spans, or numeric citation style; these errors were <4% and no further correction was performed in the larger unannotated data. Citation sentiment was omitted because negative sentiment is rare and subjective to classify.
  • Evident from the text: the classifier and analyses focus on ACL/NLP data, so generalization to other fields is not established; rare classes were oversampled during annotation.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1162/tacl_a_00028

Cites

91 of 91 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

36 Own result · 17 No basis given · 13 Own interpretation · 11 No full text of the sources · 8 Sources not found · 7 Own method · 5 Not checked yet · 1 Supported · 1 Partly supported · 1 Mixed support · accuracy not yet measured

Show all 50 claims

  • Probably not a substantial claim. 30
  • It may state two separate claims as one. 27
50 of 50
  • The paper relies on this without support.

    The number of ACL workshops increased from around ten in the 1990s to over 100 by 2010, with many workshops having multiple iterations across the years.

    “The number of workshops has increased substantially with the growth of the field, from around ten workshops in the 1990s to over 100 workshops by 2010, with many workshops having multiple iterations across the years.”
  • The paper relies on this without support.

    The growth of the ACL community has been accompanied by the creation of new publication venues.

    “The growth of the ACL community has been accompanied by the creation of new publication venues.”
  • The paper relies on this without support.

    ACL venues began allowing unlimited references in 2010 for ACL, 2011 for NAACL, etc.

    “This transition corresponds to the date at which ACL venues began allowing unlimited references (2010 for ACL, 2011 for NAACL, etc.).”
  • The paper relies on this without support.

    Many fluff citations exist, while some less common ones are substantively relevant to the paper's argument.

    “Many fluff citations exist, while some less common ones are substantively relevant to the paper's argument.”
4 of 4

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

3 of 3

Citations

Cites

Show all 79 references

Research works printed but not linked: 5

  1. Fernández-Delgado Manuel, 2014, Journal of Machine Learning Research (JMLR), 15, 3133
  2. Haas Andrew, 1989, Computational Linguistics, 15, 219
  3. Leech Geoffrey, 1992, Language Research, 28, 1
  4. Pedregosa Fabian, 2011, The Journal of Machine Learning Research (JMLR), 12, 2825
  5. Skelton John, 1994, British Journal of General Practice, 44, 455

Cited by

Sorted by citations

Items 201–234 of 234

Metadata

Authors
Jurgens, D., Kumar, S., Hoover, R., McFarland, D., & Jurafsky, D.
Affiliations
  • University of Michigan (US)
  • Stanford University (US)
  • Stanford University,
Corresponding author
  • Jurgens, David
  • Kumar, Srijan
  • Hoover, Raine
  • Jurafsky, Dan
Venue
Transactions of the Association for Computational Linguistics
Publisher
MIT Press
Published
2018-12
Volume
6
Pages
391-406
ISSN
2307-387X (ISSN-L)
OpenAlex
W2808556605
Cited by
212
References
79
Citations by year
2026: 16 · 2025: 25 · 2024: 21 · 2023: 42 · 2022: 34 · 2021: 43 · 2020: 19 · 2019: 12
FWCI
14.11
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Transactions of the Association for Computational Linguistics (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • National Science Foundation (1633036)
  • Brown Institute for Media Innovation
Topics
  • Social Sciences › Library and Information Sciences › scientometrics and bibliometrics research
  • Physical Sciences › Computer Science › Information Systems › Web visibility and informetrics
  • Social Sciences › Library and Information Sciences › Academic Writing and Publishing
Keywords
citation functions, citation analysis, citation counts, field evolution, publication venues, discourse structure, citation classification, natural language processing, disciplinary consensus

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

A language model drew the claims from the paper on 2026-11-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).

A second model labelled each claim on 2026-11-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).

Cited sources are checked with a language model’s help (gpt-6-sol).

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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