Citation impact prediction for scientific papers using stepwise regression analysis

Yu, Tianwei; Yu, Guang; Li, Pengyu; Wang, Liang

doi:10.1007/s11192-014-1279-6

Cited by 137 publications

(85 citation statements)

References 31 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%

“…It is worth noting that three described categories overlap, i.e., some methods belong to more than one category. For example, some existing works utilize both short-term citation counts and the author properties (Yu et al, 2014;Castillo et al, 2007) and therefore, they belong to both the second and the third illustrated categories.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

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%

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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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“…Many other studies have also employed the linear regression to model these count variables (Ajiferuke, 2005;Ayres & Vars, 2000;Bornmann & Daniel, 2007;Habibzadeh & Yadollahie, 2010;Landes & Posner, 2000;Lokker, McKibbon, McKinlay, Wilczynski, & Haynes, 2008;Peters & van Raan, 1994;Vaughan & Thelwall, 2005;Willis et al, 2011;Xia, Myers, & Wilhoite, 2011;Yoshikane, Suzuki, Arakama, Ikeuchi, & Tsuji, 2013;Yu, Yu, Li, & Wang, 2014). However, negative binomial regression models, which are a type of count regression models, have been used in a very few informetric studies (Baccini, Barabesi, Cioni, & Pisani, 2014;Chen, 2012;Didegah & Thelwall, 2013;Lee, Lee, Song, & Lee, 2007;McDonald, 2007;Thelwall & Maflahi, 2015;Walters, 2006;Yoshikane, 2013;Yu & Wu, 2014).…”

Section: Introductionmentioning

confidence: 99%

Modelling count response variables in informetric studies: Comparison among count, linear, and lognormal regression models

Ajiferuke

Famoye

2015

Journal of Informetrics

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“…Only a small set then survives the selection process (Boyack and Klavans 2014;Simonton 1997;Uzzi et al 2013). Nor does the selection process end here because not all scientific publications have the same impact on the scientific community, as judged by citation indices (Fortunato et al 2018;Roth et al 2012;Yu et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Qualifying threshold of “take-off” stage for successfully disseminated creative ideas

et al. 2019

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The creative process is essentially Darwinian and only a small proportion of creative ideas are selected for further development. However, the threshold that identifies this small fraction of successfully disseminated creative ideas at their early stage has not been thoroughly analyzed through the lens of Rogers's innovation diffusion theory. Here, we take highly cited (top 1%) research papers as an example of the most successfully disseminated creative ideas and explore the time it takes and citations it receives at their "take-off" stage, which play a crucial role in the dissemination of creativity. Results show the majority of highly cited papers will reach 10% and 25% of their total citations within two years and four years, respectively. Interestingly, our results also present a minimal number of articles that attract their first citation before publication. As for the discipline, number of references, and Price index, we find a significant difference exists: Clinical, Pre-Clinical & Health and Life Sciences are the first two disciplines to reach the C10% and C25% in a shorter amount of time. Highly cited papers with limited references usually take more time to reach 10% and 25% of their total citations. In addition, highly cited papers will attract citations rapidly when they cite more recent references. These results provide insights into the timespan and citations for a research paper to become highly cited at the "take-off" stage in its diffusion process, as well as the factors that may influence it.

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Citation impact prediction for scientific papers using stepwise regression analysis

Cited by 137 publications

References 31 publications

Predicting citation counts based on deep neural network learning techniques

Predicting citation counts based on deep neural network learning techniques

Modelling count response variables in informetric studies: Comparison among count, linear, and lognormal regression models

Qualifying threshold of “take-off” stage for successfully disseminated creative ideas

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