Article length and citation outcomes

Hasan, Syed; Breunig, Robert

doi:10.1007/s11192-021-04083-x

Cited by 13 publications

(8 citation statements)

References 53 publications

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“…This relationship has been found in immunology and surgery (Weale et al, 2004), ecology (Fox et al, 2016), sociology, applied physics, general medicine (Van Wesel et al, 2014), biology and biochemistry, chemistry, mathematics, physics (Vieira & Gomes, 2010), psychology (Haslam et al, 2008), psychiatry (Hafeez et al, 2019), medicine (Falagas et al, 2013), management (Mingers & Xu, 2010), and social sciences (Hodge et al, 2017), as well as for a multidisciplinary set of 1.3 million articles published in 2012 (Haustein et al, 2015). The same positive relationship has also been found for many journals including New England Journal of Medicine, Journal of the American Medical Association, the Lancet (Lyu & Wolfram, 2018), and five economics journals (Hasan & Breunig, 2021). A meta-analysis of 18 relevant studies found a moderate positive correlation (r = 0.310) between article length and citations (Xie et al, 2019).…”

Section: Article Lengthmentioning

confidence: 63%

Factors associating with or predicting more cited or higher quality journal articles: An Annual Review of Information Science and Technology (ARIST) paper

Kousha

Thelwall

2023

Asso for Info Science & Tech

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Identifying factors that associate with more cited or higher quality research may be useful to improve science or to support research evaluation. This article reviews evidence for the existence of such factors in article text and metadata. It also reviews studies attempting to estimate article quality or predict long‐term citation counts using statistical regression or machine learning for journal articles or conference papers. Although the primary focus is on document‐level evidence, the related task of estimating the average quality scores of entire departments from bibliometric information is also considered. The review lists a huge range of factors that associate with higher quality or more cited research in some contexts (fields, years, journals) but the strength and direction of association often depends on the set of papers examined, with little systematic pattern and rarely any cause‐and‐effect evidence. The strongest patterns found include the near universal usefulness of journal citation rates, author numbers, reference properties, and international collaboration in predicting (or associating with) higher citation counts, and the greater usefulness of citation‐related information for predicting article quality in the medical, health and physical sciences than in engineering, social sciences, arts, and humanities.

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Section: Article Lengthmentioning

confidence: 63%

Factors associating with or predicting more cited or higher quality journal articles: An Annual Review of Information Science and Technology (ARIST) paper

Kousha

Thelwall

2023

Asso for Info Science & Tech

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“…Page count : The number of pages that an article covers. The length of an article has been shown to have a positive effect on citation counts, with a possible explanation that longer articles have more information (Leimu & Koricheva, 2005) including both theory and empirical elements (Hasan & Breunig, 2021).…”

Section: Methodsmentioning

confidence: 99%

Opening research data contributes to the citations of related research articles: Evidence from Data in Brief

Tian

Cheng-zhi

et al. 2023

Learned Publishing

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This paper explores the effect of publishing a data paper in the Open Access journal Data in Brief (DIB) on the citation counts of the related research paper. Using regression analysis, citation content analysis and a survey method, we investigate whether research papers with a related data paper have higher citation counts and the potential reasons.After controlling variables that correlate with the citation counts, research papers with a related data paper were found to have higher citation counts than those published in the same issue of the same journal. Next, we explored the causal relationship between the two variables by surveying the corresponding authors of 618 papers who shared datasets in DIB from 2014 to 2021. The results show that the authors acknowledge the benefits of sharing data in DIB, including citation increase and career reputation enhancement. We further explored how the data papers in DIB increase the citations of the related research papers by using citation content analysis. We found that scientists co-cite the data papers and their related research papers for the purpose of reusing the underlying data or portraying a better understanding of the underlying data and related research articles.

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“…Research on the prediction of highly cited academic papers mainly uses statistical features. For example, Hasan and Breunig (2021) studied the relationship between paper length and citation counts. Mammola et al (2021) extracted seven numerical features from a reference list and analyzed the relationship between the reference features and citation counts.…”

Section: Feature Framework For Highly Cited Academic Paper Predictionmentioning

confidence: 99%

Predictable by publication: discovery of early highly cited academic papers based on their own features

Tang

Zhou

2023

LHT

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PurposePredicting highly cited papers can enable an evaluation of the potential of papers and the early detection and determination of academic achievement value. However, most highly cited paper prediction studies consider early citation information, so predicting highly cited papers by publication is challenging. Therefore, the authors propose a method for predicting early highly cited papers based on their own features.Design/methodology/approachThis research analyzed academic papers published in the Journal of the Association for Computing Machinery (ACM) from 2000 to 2013. Five types of features were extracted: paper features, journal features, author features, reference features and semantic features. Subsequently, the authors applied a deep neural network (DNN), support vector machine (SVM), decision tree (DT) and logistic regression (LGR), and they predicted highly cited papers 1–3 years after publication.FindingsExperimental results showed that early highly cited academic papers are predictable when they are first published. The authors’ prediction models showed considerable performance. This study further confirmed that the features of references and authors play an important role in predicting early highly cited papers. In addition, the proportion of high-quality journal references has a more significant impact on prediction.Originality/valueBased on the available information at the time of publication, this study proposed an effective early highly cited paper prediction model. This study facilitates the early discovery and realization of the value of scientific and technological achievements.

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Article length and citation outcomes

Cited by 13 publications

References 53 publications

Factors associating with or predicting more cited or higher quality journal articles: An Annual Review of Information Science and Technology (ARIST) paper

Factors associating with or predicting more cited or higher quality journal articles: An Annual Review of Information Science and Technology (ARIST) paper

Opening research data contributes to the citations of related research articles: Evidence from Data in Brief

Predictable by publication: discovery of early highly cited academic papers based on their own features

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