Analyzing linguistic complexity and scientific impact

Lu, Caiyi; Bu, Yi; Dong, Xisong; Wang, Jie; Ding, Ying; Larivière, Vincent; Sugimoto, Cassidy R.; Paul, Logan; Zhang, Chengzhi

doi:10.1016/j.joi.2019.07.004

Cited by 43 publications

(30 citation statements)

References 55 publications

(84 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In linguistics, there is a myriad of measures of writing quality, ranging from basic readability indices (length of syllables, words, sentences, number of complex words and so on) to the use of adjectives or adverbs (Okulicz-Kozaryn 2013) and linguistic complexity (Lu et al 2019). In this paper, I focus on traditional readability measures and throughout the paper I use the terms "higher readability" and "better-written" interchangeably.…”

Section: Methodsmentioning

confidence: 99%

Write better, publish better

Fages

2020

Scientometrics

View full text Add to dashboard Cite

There is evidence that having more readable abstracts and introductions help authors get cited. I show that, in economics, there is also an effect of readability on the probability of publishing in a Top 5 journal (and in a higher-ranked journal in general). I compute readability measures for a set of working papers and examine the journals in which they get published. My results suggest that previous estimates of the effect on citations are downward biased, as higher-ranked journals are more widely read and cited.

show abstract

Section: Methodsmentioning

confidence: 99%

Write better, publish better

Fages

2020

Scientometrics

View full text Add to dashboard Cite

show abstract

“…In strategy II, we follow Guo et al [6] and stipulate that each group of publications has an equal number of total citations. In strategy III, we define highly cited publications as the top 1%, medium cited as 1%–10% and lowly cited as those after 10% [3]. The empirical results of the three strategies are shown in Table 1.…”

Section: Resultsmentioning

confidence: 99%

“…Studying highly cited publications has become a tradition in bibliometrics, and most articles in bibliometrics have to deal with the choice of partitioning publications into different categories (e.g. highly, medium or lowly cited) [2–5]. However, the majority of these studies have chosen these different categories by establishing artificial thresholds.…”

Section: Introductionmentioning

confidence: 99%

Partitioning highly, medium and lowly cited publications

Huang

Ding³

et al. 2020

Journal of Information Science

Self Cite

View full text Add to dashboard Cite

Dividing papers based on their numbers of citations into several groups constitutes one of the most common research practices in bibliometrics and beyond. However, existing dividing methods are both arbitrary and subject to bias. This article proposes a novel approach to partition highly, medium and lowly cited publications based on their citation distribution. We utilise the whole Web of Science (WoS) dataset to demonstrate how to apply this approach to scholarly datasets and examine the robustness of our algorithm in each of the six disciplines under the WoS dataset. The codes that underlie the algorithm are available online.

show abstract

“…Shorter titles have a higher citation frequency, and the conclusion is: keep the title short. Linguistic complexity of title, abstract and main text has also been studied in relation with citation frequency [ 20 , 25 – 28 ]. While no difference was found between citation frequency and linguistic complexity of the main text [ 28 ], top ranked journals use a simple language in title and abstract [ 27 ].…”

Section: Discussionmentioning

confidence: 99%

“…Linguistic complexity of title, abstract and main text has also been studied in relation with citation frequency [ 20 , 25 – 28 ]. While no difference was found between citation frequency and linguistic complexity of the main text [ 28 ], top ranked journals use a simple language in title and abstract [ 27 ]. However, scientific articles are generally difficult to read [ 29 , 30 ].…”

Section: Discussionmentioning

confidence: 99%

Empirical analysis of the text structure of original research articles in medical journals

2020

View full text Add to dashboard Cite

Successful publishing of an article depends on several factors, including the structure of the main text, the so-called introduction, methods, results and discussion structure (IMRAD). The first objective of our work is to provide recent results on the number of paragraphs (pars.) per section used in articles published in major medical journals. Our second objective is the investigation of other structural elements, i.e., number of tables, figures and references and the availability of supplementary material. We analyzed data from randomly selected original articles published in years 2005, 2010 and 2015 from the journals The BMJ, The Journal of the American Medical Association, The Lancet, The New England Journal of Medicine and PLOS Medicine. Per journal and year 30 articles were investigated. Random effect meta-analyses were performed to provide pooled estimates. The effect of time was analyzed by linear mixed models. All articles followed the IMRAD structure. The number of pars. per section increased for all journals over time with 1.08 (95% confidence interval (CI): 0.70-1.46) pars. per every two years. The largest increase was observed for the methods section (0.29 pars. per year; 95% confidence interval (CI): 0.19-0.39). PLOS Medicine had the highest number of pars. The number of tables did not change, but number of figures and references increased slightly. Not only the standard IMRAD structure should be used to increase the likelihood for publication of an article but also the general layout of the target journal. Supplementary material has become standard. If no journal-specific information is available, authors should use 3/10/9/8 pars. for the introduction/methods/results/ discussion sections.

show abstract

Analyzing linguistic complexity and scientific impact

Cited by 43 publications

References 55 publications

Write better, publish better

Write better, publish better

Partitioning highly, medium and lowly cited publications

Empirical analysis of the text structure of original research articles in medical journals

Contact Info

Product

Resources

About