A data analytic approach to quantifying scientific impact

Cao, Xuanyu; Chen, Yan; Liu, K. J. Ray

doi:10.1016/j.joi.2016.02.006

Cited by 51 publications

(32 citation statements)

References 29 publications

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“…Many studies exist in the literature for predicting the impact and success of scientific works. The existing works aim different goals such as citation count prediction for scientific papers (Dong et al, 2015;Cao et al, 2016;Wang et al, 2013;Yu et al, 2014;Yan et al, 2011;Mazloumian, 2012;Dong et al, 2016;Yan et al, 2012;Castillo et al, 2007;Lamb et al, 2018;Pobiedina and Ichise, 2016;Bornmann et al, 2014), predicting highly cited papers (McNamara et al, 2013;Newman, 2014;Sarigöl et al, 2014), predicting h-index of the researchers (Ayaz et al, 2018;Dong et al, 2016;Acuna et al, 2012), and predicting the impact factor of scientific journals (Ketcham, 2007;Wu et al, 2008;Rocha-e Silva, 2016).…”

Section: Related Workmentioning

confidence: 99%

“…For example, Lamb et al (2018) investigate the popularity of a paper in the web (e.g., in the news and Wikipedia) and social networks (such as Twitter and Facebook), and then show a correlation between this popularity and the amount of citation to the paper. As the main class of interest, many existing works utilize short-term citation count of a paper for predicting its long-term citations (Newman, 2014;Wang et al, 2013;Cao et al, 2016;Yu et al, 2014). The citation count of the early years after publication is an important feature which may contribute to improve the accuracy of citation count prediction methods (Kosteas, 2018;Bornmann et al, 2014).…”

Section: Related Workmentioning

confidence: 99%

“…But in recent years, artificial intelligence and machine learning methods are widely utilized to replace human-inspired constant models. For example, Cao et al predict citation count of a paper using the citation pattern of the most similar existing papers according to the citations of the early years after publication (Cao et al, 2016). This method first clusters the papers according to their similarity in short-term citation patterns, and then predicts the citation count based on the average and the centroid of the clusters.…”

Section: Related Workmentioning

confidence: 99%

“…Although some existing works include more sources of information as the inputs of the citation prediction method, we limit the inputs as simple as the citation pattern of the early publication year, in order to keep the problem definition simple, general, and also applicable in other domains. This problem has already gained attention in the literature, and various methods are proposed to solve this problem (Cao et al, 2016;Wang et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Predicting citation counts based on deep neural network learning techniques

Abrishami

Aliakbary

2019

Journal of Informetrics

113

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With the growing number of published scientific papers world-wide, the need to evaluation and quality assessment methods for research papers is increasing. Scientific fields such as scientometrics, informetrics, and bibliometrics establish quantified analysis methods and measurements for evaluating scientific papers. In this area, an important problem is to predict the future influence of a published paper. Particularly, early discrimination between influential papers and insignificant papers may find important applications. In this regard, one of the most important metrics is the number of citations to the paper, since this metric is widely utilized in the evaluation of scientific publications and moreover, it serves as the basis for many other metrics such as h-index. In this paper, we propose a novel method for predicting long-term citations of a paper based on the number of its citations in the first few years after publication. In order to train a citation count prediction model, we employed artificial neural network which is a powerful machine learning tool with recently growing applications in many domains including image and text processing. The empirical experiments show that our proposed method outperforms state-of-the-art methods with respect to the prediction accuracy in both yearly and total prediction of the number of citations.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting citation counts based on deep neural network learning techniques

Abrishami

Aliakbary

2019

Journal of Informetrics

113

View full text Add to dashboard Cite

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“…[12] which managed to characterize the novelty of a paper through diversity (frequency of atypical combinations) of its references. A posteriori factors develop during short time after the paper has been published and these include the "impact factor"-the number of citations during a short period after publication [4,[16][17][18][19], and the place that the paper occupies in its community.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Citation Dynamics of Individual Papers

Golosovsky

2019

SpringerBriefs in Complexity

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Understanding and predicting future research impact at different career stages—A social network perspective

Zuo

Zhao

2020

Asso for Info Science & Tech

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Performance assessment is ubiquitous and crucial in people analytics. Scientific impact, particularly, plays a significant role in the academia. This paper attempts to understand researchers' career trajectories by considering the research community as a social network, where individuals build ties with each other via coauthorship. The resulting linkage facilitates information flow and affects researchers' future impact. Consequently, we systematically investigate the career trajectories of researchers with respect to research impact using the social capital theory as our theoretical foundation. Specifically, for early‐stage and mid‐career academics, we find that connections with prominent researchers associate with greater impact. Brokerage positions, in addition, are beneficial to a researcher's research impact in the long run. For senior researchers, however, the only social network feature that significantly affects their future impact is the reputation of their recently built ties. Finally, we build predictive models on future research impact which can be leveraged by both organizations and individuals. This paper provides empirical evidence for how social networks provide signals on researchers' career dynamics guided by social capital theory. Our findings have implications for individual researchers to strategically plan and promote their careers and for research institutions to better evaluate current as well as prospective employees.

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A data analytic approach to quantifying scientific impact

Cited by 51 publications

References 29 publications

Predicting citation counts based on deep neural network learning techniques

Predicting citation counts based on deep neural network learning techniques

Prediction of Citation Dynamics of Individual Papers

Understanding and predicting future research impact at different career stages—A social network perspective

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