Do new research issues attract more citations? A comparison between 25 Scopus subject categories

Thelwall, Michael; Sud, Pardeep

doi:10.1002/asi.24401

Cited by 10 publications

(11 citation statements)

References 38 publications

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“…The novelty together with the volume of publications attract other researchers' attention and increase visibility and impact (Tu & Seng, 2012). We have not studied novelty in this paper but Thelwall and Sud (2021) found that articles published around novel topics have a citation advantage using a word frequency analysis. It would be interesting to study topic novelty and growth simultaneously for their citation advantage in a future study.…”

Section: Discussionmentioning

confidence: 92%

“…and fast growth together with novelty is predicted to lead to prominent impact in the future (Rotolo et al, 2015). Prior studies have already confirmed the association between novelty (newness) and citation impact and showed that new research has a citation advantage (Porter et al, 2019), although it may significantly vary across different subject domains (Thelwall & Sud, 2021).…”

Section: Introductionmentioning

confidence: 93%

“…For instance, the introduction of the h-index by Hirsch in 2005 led to a rapid growth of articles and led to a short term increase in citations in this topic, referred to as a "bubble" by Rousseau et al (2013). Thelwall and Sud (2021) showed that new research topics are cited more in some disciplines and emerging topics were found to benefit from both within-and outside-field citation links (Kwon et al, 2019). Bornmann and Tekles (2021) argue that new topics are built upon so called disruptive publications, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…publications characterized by their ability to overthrow established thinking (Bornmann et al, 2020). Newness (or novelty) together with the fast growth of publications in an area is predicted to lead to prominent impact in the future (Rotolo et al, 2015) and recent research has shown that new topics have a citation advantage (Thelwall & Sud, 2021). However, the association between the growth of the publication output in a research publication and the citation rates within the field has not been thoroughly investigated.…”

Section: Introductionmentioning

confidence: 99%

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The association between topic growth and citation impact of research publications

Sjögårde

Didegah

2022

Scientometrics

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Citations are used for research evaluation, and it is therefore important to know which factors influence or associate with citation impact of articles. Several citation factors have been studied in the literature. In this study we propose a new factor, topic growth, that no previous study has studied empirically. The growth rate of topics may influence future citation counts because a high growth in a topic means there are more publications citing previous publications in that topic. We construct topics using community detection in a citation network and use a two-part regression model to study the association between topic growth and citation counts in eight broad disciplines. The first part of the model uses quantile regression to estimate the effect of growth ratio on citation counts for publications with more than three citations. The second part of the model uses logistic regression to model the influence of the explanatory variables on the probability of being lowly cited versus being modestly or highly cited. Both models control for three variables that may distort the association between the topic growth and citations: journal impact, number of references, and number of authors. The regression model clearly shows that publications in fast-growing topics have a citation advantage compared to publications in slow-growing or declining topics in all of the eight disciplines. Using citation indicators for research evaluation may give incentives for researchers to publish in fast-growing topics, but they may cause research to be less diversified. The results have also some implications for citation normalization.

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Section: Discussionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The association between topic growth and citation impact of research publications

Sjögårde

Didegah

2022

Scientometrics

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“…Citing papers is a common way to express appreciation of someone else's research or to acknowledge the relevance thereof to our own results [1]. Regardless of the true motivation [2], the what-to-cite decisions of individual authors can be averaged over the whole bibliographic networks.…”

Section: Introductionmentioning

confidence: 99%

Power Laws, the Price Model, and the Pareto type-2 Distribution

Siudem,

Nowak,

Gagolewski

2022

Preprint

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We consider a version of the D.J.Price's model for the growth of a bibliographic network, where in each iteration a constant number of citations is randomly allocated according to a weighted combination of accidental (uniformly distributed) and preferential (rich-get-richer) rules. Instead of relying on the typical master equation approach, we formulate and solve this problem in terms of the rank-size distribution. We show that, asymptotically, such a process leads to a Pareto-type 2 distribution with an appealingly interpretable parametrisation. We prove that the solution to the Price model expressed in terms of the rank-size distribution coincides with the expected values of order statistics in an independent Paretian sample. We study the bias and the mean squared error of three well-behaving estimators of the underlying model parameters. An empirical analysis of a large repository of academic papers yields a good fit not only in the tail of the distribution (as it is usually the case in the power law-like framework), but also across the whole domain. Interestingly, the estimated models indicate higher degree of preferentially attached citations and smaller share of randomness than previous studies.

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Bibliometric research analysis of molecularly imprinted polymers (MIPs): evidence and research activity dynamics

Mittas,

Gkika,

Georgiou

et al. 2023

Environ Sci Pollut Res

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The escalating issue of water pollution has become a worldwide issue that has captured the attention of numerous scientists. Molecularly imprinted polymers (MIPs) have emerged as adaptable materials with exceptional attributes, including easy synthesis, low cost, remarkable durability, long life, and accessibility. These attributes have motivated researchers to develop novel materials based on MIPs to tackle hazardous contaminants in environmental matrices. The purpose of this paper was to conduct a bibliometric analysis on MIPs’ publications, in order to shed light on the developments and focus points of the field. The selected publications were obtained from Scopus database and subjected to a filtering process, resulting in 11,131 relevant publications. The analysis revealed that the leading publication source (journal) is Biosensors and Bioelectronics; the mostly employed keywords are solid-phase extraction, electrochemical sensor, and molecular recognition; and the top contributing countries are China, Iran, and the USA. The Latent Dirichlet Allocation (LDA) algorithm was used for extracting thematic axes from the textual content of the publications. The results of the LDA model showcase that the topic of synthesis and performance of MIPs for environmental applications can be considered as the most dominant topic with a share value of 72.71%. From the analysis, it can be concluded that MIPs are a cross-disciplinary research field.

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Do new research issues attract more citations? A comparison between 25 Scopus subject categories

Cited by 10 publications

References 38 publications

The association between topic growth and citation impact of research publications

The association between topic growth and citation impact of research publications

Power Laws, the Price Model, and the Pareto type-2 Distribution

Bibliometric research analysis of molecularly imprinted polymers (MIPs): evidence and research activity dynamics

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