Dataset Information

Words by the tail: Assessing lexical diversity in scholarly titles using frequency-rank distribution tail fits.

ABSTRACT: This research assesses the evolution of lexical diversity in scholarly titles using a new indicator based on zipfian frequency-rank distribution tail fits. At the operational level, while both head and tail fits of zipfian word distributions are more independent of corpus size than other lexical diversity indicators, the latter however neatly outperforms the former in that regard. This benchmark-setting performance of zipfian distribution tails proves extremely handy in distinguishing actual patterns in lexical diversity from the statistical noise generated by other indicators due to corpus size fluctuations. From an empirical perspective, analysis of Web of Science (WoS) article titles from 1975 to 2014 shows that the lexical concentration of scholarly titles in Natural Sciences & Engineering (NSE) and Social Sciences & Humanities (SSH) articles increases by a little less than 8% over the whole period. With the exception of the lexically concentrated Mathematics, Earth & Space, and Physics, NSE article titles all increased in lexical concentration, suggesting a probable convergence of concentration levels in the near future. As regards to SSH disciplines, aggregation effects observed at the disciplinary group level suggests that, behind the stable concentration levels of SSH disciplines, a cross-disciplinary homogenization of the highest word frequency ranks may be at work. Overall, these trends suggest a progressive standardization of title wording in scientific article titles, as article titles get written using an increasingly restricted and cross-disciplinary set of words.

SUBMITTER: Berube N

PROVIDER: S-EPMC6037356 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Words by the tail: Assessing lexical diversity in scholarly titles using frequency-rank distribution tail fits.

Bérubé Nicolas N Sainte-Marie Maxime M Mongeon Philippe P Larivière Vincent V

PloS one 20180709 7

This research assesses the evolution of lexical diversity in scholarly titles using a new indicator based on zipfian frequency-rank distribution tail fits. At the operational level, while both head and tail fits of zipfian word distributions are more independent of corpus size than other lexical diversity indicators, the latter however neatly outperforms the former in that regard. This benchmark-setting performance of zipfian distribution tails proves extremely handy in distinguishing actual pat ...[more]

PMID: 29985920

Similar Datasets

Project description:ImportanceThe language of medical research appears to be intrinsically tied to the culture of medical research and provides a unique window into broader trends in the culture of medicine.ObjectiveTo analyze medical language from 5 premier medical journals and investigate broader changes in the culture of clinical investigation during the last 40 years.Design, setting, and participantsIn this qualitative study using a data-driven analysis, 302 293 PubMed records were extracted from JAMA, The Lancet, Annals of Internal Medicine, the BMJ, and New England Journal of Medicine from January 1, 1976, through December 31, 2015, to identify key trends in medical language. A frequency analysis was applied across the 40-year time frame in JAMA to assess the major trends in all publication types. Patient-centered language was analyzed in clinical trials in the flanking time periods (1976-1980 and 2011-2015) across the 5 journals. Data were analyzed from November 16, 2016, through November 9, 2018.Main outcomes and measuresIncreasing or decreasing frequency of words (monograms) and word pairs (bigrams) and the proportion of patient-centric words in journal article titles.ResultsIn JAMA, 50 277 articles of all publication types were included. In the frequency analysis, the most increased terms were reflective of the language of epidemiological research. The bigram analysis revealed a decline in causal language (-2.42/100 000 words to -2.03/100 000 words; false discovery rate [FDR], <0.01) and an increased description of patients in the plural form (6.92/100 000 words to 11.4/100 000 words; FDR, <0.01). A trend to separate patient from disease was observed; for example, there was a decrease in describing a patient as a diabetic (-2.21/100 000 words; FDR, <0.01) compared with a patient with diabetes. In the analysis of clinical trials in all 5 journals, 3125 titles were identified (range, 193-932 per journal). In 4 of the 5 journals, use of patient-centric keywords increased significantly (absolute increase, 18.9%-34.3%; P < .001 for 3 journals; P = .01 for 1 journal), with the New England Journal of Medicine as the exception. This finding reflects a change from shorter disease-centric titles to longer titles that describe patients with a disease.Conclusions and relevanceTrends in medical language reflect the rise of evidence-based medicine, a shift in focus from individuals to populations, and a separation of patient and disease. Data-driven analysis of medical language provides a unique window into the changing landscape of medical culture.

Dataset Information

Words by the tail: Assessing lexical diversity in scholarly titles using frequency-rank distribution tail fits.

Publications

Words by the tail: Assessing lexical diversity in scholarly titles using frequency-rank distribution tail fits.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets