A deep-learning based citation count prediction model with paper metadata semantic features

Ma, Anqi; Liu, Yu; Xu, Xiujuan; Dong, Tao

doi:10.1007/s11192-021-04033-7

Cited by 30 publications

(20 citation statements)

References 58 publications

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“…General medicine (Falagas et al, 2013), internal medicine (Fu & Aliferis, 2010), clinical medicine (Lokker et al, 2008), bioinformatics (Ib añez et al, 2009), high energy physics theory (Chen & Zhang, 2015;Zhao & Feng, 2022), physics (Zhao & Feng, 2022), environmental science and management (Vanclay, 2013), AI (Cummings & Nassar, 2020;Li et al, 2019;Ma et al, 2021), computer and information science (regression: Lee, 2020), library, information and documentation (Ruan et al, 2020;Yu et al, 2014), Markov chains (Xu et al, 2019), mixed (Abrishami & Aliakbary, 2019), or all (Akella et al, 2021). There are wide differences between fields in the accuracy of citation count predictions because of differences in the extent to which input factors systematically associate with higher citation counts.…”

Section: Fields Coveredmentioning

confidence: 99%

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: Fields Coveredmentioning

confidence: 99%

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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“…With the re-emergence of the artificial intelligence, neural network-based methods such as the multilayer perceptron neural network with the Back Propagation (BP) algorithm (Ruan et al, 2020), the transformer (Huang et al, 2022), the Doc2vec and LSTM (Ma et al, 2021), the recurrent neural network (Abrishami & Aliakbary, 2019), and the convolutional neural network (Xu et al, 2019), have been increasingly used to predict citation count of academic papers and achieved acceptable performance. These neural network-based methods have strong generality and robustness, and they don't require features to be independent or the data to be normally distributed.…”

Section: Citation Count Prediction Of Academic Papers (Ccpap)mentioning

confidence: 99%

“…Previously, citation count prediction of academic papers (CCPAP) has been widely studied with classification or regression models in the field of bibliometrics, and various factors have been demonstrated to be highly related to the citation counts of research papers, such as the writing style, the length of the abstract, and the research topic (Abrishami & Aliakbary, 2019;Huang et al, 2022;Li et al, 2015;Ma et al, 2021;Ruan et al, 2020). However, few studies have explored the clinical citation count prediction of biomedical papers (CCCPBP) since there was no comprehensive and reliable database that tracks citations among biomedical papers (Liang et al, 2021;Xu et al, 2020;Yu et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Predicting the clinical citation count of biomedical papers using multilayer perceptron neural network

Tang²,

Cheng³

2022

Journal of Informetrics

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“…Assessing scholarly impact has been used to address many realworld issues like funding allocation, promotion decision, and award evaluation [30,44]. It is acknowledged that citations are a widely used measure of scientific impact, but long-term prediction of citation number is very challenging and has become an emerging applied research topic [28,31,41]. The skewed distribution of citations tends to follow the power law or lognormal distribution [40].…”

Section: Evaluation Of Scholarly Impactmentioning

confidence: 99%

The significance and impact of winning an academic award

Ren

Shi

Shatte

et al. 2022

Proceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries

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Academic award plays an important role in an academic's career, particularly for early career academics. Previous studies have primarily focused on the impact of awards conferred to academics who have made outstanding contributions to a specific research field, such as the Nobel Prize. In contrast, this paper aims to investigate the effect of awards conferred to academics at an earlier career stage, who have the potential to make a great impact in the future. We devise a metric named Award Change Factor (ACF), to evaluate the change of a recipient's academic behavior after winning an academic award. Next, we propose a model to compare award recipients with academics who have similar performance before winning an academic award. In summary, we analyze the impact of an award on the recipients' academic impact and their teams from different perspectives. Experimental results show that most recipients do have improvements in both productivity and citations after winning an academic award, while there is no significant impact on publication quality. In addition, receipt of an academic award not only expands recipients' collaboration network, but also has a positive effect on their team size.

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A deep-learning based citation count prediction model with paper metadata semantic features

Cited by 30 publications

References 58 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

Predicting the clinical citation count of biomedical papers using multilayer perceptron neural network

The significance and impact of winning an academic award

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