Predicting citation counts based on deep neural network learning techniques

Abrishami, Ali; Aliakbary, Sadegh

doi:10.1016/j.joi.2019.02.011

Cited by 110 publications

(66 citation statements)

References 47 publications

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“…In their study, Abrishami and Aliakbary (2019) utilized a machine learning tool to present a model for predicting the long‐term citations of scientific papers based on the number of citations in the first few years of their publication. Based on their experiments, they claimed that their proposed method provided more accurate predictions compared to the other advanced methods.…”

Section: Literature Reviewmentioning

confidence: 99%

The study of scientific map and thematic structure of social work

Negahban

2020

Asian Soc Wrk & Pol Rev

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Although scientific production alone cannot be regarded as a definite mark of comprehensive scientific growth and development, many R&D decisions today are in fact based on mapping and measuring the countries' scientific output. In view of its dynamic nature and its direct link to technology and scientific development, Social media has emerged as an important area of science and is offered as an academic discipline in universities up to the doctoral level. Today, the development of Social media and social policy plays a decisive role in the development of scientific knowledge, and the research activities in this field have progressed in parallel with the activities in other disciplines. The researchers in this field have published a substantial number of scholarly papers in credible scientific databases, which have over time led to the formation of its scientific structure. Mapping the scientific and social structure of this field presents a valuable source of information about the position of each of its subfields, signifying its overall power (Negahban & Mohammadyan, 2014).Map of science deals with the development and application of computational techniques to the visualization, analysis, and modeling of a broad range of scientific and technological activities as a whole. A science map provides an overview of the scientific landscape which AbstractThis study investigated the scientific map and thematic structure of Social media.It is a scientometrics research, using quantitative method and citation analysis. In order to draw a scientific map of the research literature in the field of Social media, the data were extracted from the Web of Science database within a specified time period. The retrieved records were formatted using WC10.exe conversion software and were then mapped using VOSviewer software. The findings revealed a growing trend in the number of articles published in the field of Social media, and this number increases on an annual basis. The most widely used subject areas in the field of Social media were identified. High-frequency keywords such as Social media, education, sociology, social justice, women studies, and psychiatry are the newly emerging and active concepts in this field. Regarding the widely used subject areas in the field of Social media, it was noted that the two areas of Social media and education are ranked first and second as the most frequently used terms. In this study, by the conceptual map found in the field of Social media, it can be concluded that the concepts are highly interconnected. Furthermore, by the interconnectivity and closeness of centrality measures of social concepts illustrated in the provided map, it can be argued that the highest values are attributed to the subject areas of Social media and education. K E Y W O R D Snetwork analysis, scientific map, scientific productions, social work 2003-2014. Leyesdorff, Carley, and Rafols (2012 initially analyzed the differences and similarities among the thematic categories of Web of Science in versions 4 and 5. Zhang et...

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Section: Literature Reviewmentioning

confidence: 99%

The study of scientific map and thematic structure of social work

Negahban

2020

Asian Soc Wrk & Pol Rev

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“…Following works formally defined this task and thoroughly examined various possible factors correlated with citation counts (Yan et al, 2011;Bhat et al, 2015;Chen and Zhang, 2015;Singh et al, 2015;Chen and Zhang, 2015;Park et al, 2017). These studies mainly model the long-term scientific impact (Wu et al, 2019;Abrishami and Aliakbary, 2018;Yuan et al, 2018). Furthermore, some researchers casted the problem as a time series task, and focused on analyzing temporal features or patterns in the process of citation growth (Davletov et al, 2014;Xiao et al, 2016;Yuan et al, 2018).…”

Section: Related Workmentioning

confidence: 99%

A Neural Citation Count Prediction Model based on Peer Review Text

Zhao

Yin

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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Citation count prediction (CCP) has been an important research task for automatically estimating the future impact of a scholarly paper. Previous studies mainly focus on extracting or mining useful features from the paper itself or the associated authors. An important kind of data signals, i.e., peer review text, has not been utilized for the CCP task. In this paper, we take the initiative to utilize peer review data for the CCP task with a neural prediction model. Our focus is to learn a comprehensive semantic representation for peer review text for improving the prediction performance. To achieve this goal, we incorporate the abstractreview match mechanism and the cross-review match mechanism to learn deep features from peer review text. We also consider integrating hand-crafted features via a wide component. The deep and wide components jointly make the prediction. Extensive experiments have demonstrated the usefulness of the peer review data and the effectiveness of the proposed model. Our dataset has been released online.

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“…Yan et al [32] and others used machine learning algorithms for citation count prediction, including CART, SVM, MLR. Latest, in-depth learning, such as RNNs, was introduced to learn a prediction model based on the sequence pattern of the citations with early information [33]. However, only instantaneous citation features are used to modeling RNN.…”

Section: Related Workmentioning

confidence: 99%

Early Prediction of Scientific Impact Based on Multi-Bibliographic Features and Convolutional Neural Network

Jiang

et al. 2019

IEEE Access

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The increasingly available large-scale bibliographic data that generate a heterogeneous network provide opportunities to detect, track, and predict the evolution of science. Recently, many efforts have been devoted to quantifying the impact of scientific papers within different citation time windows. However, the complex patterns of the citation network make it difficult to predict future citations on the basis of a short time window. Accordingly, we present a data-centric methodology to predict long-term scientific impact by combining numerous bibliographic features and convolutional neural network. More specifically, we first expand the input features from the annual citation records to the features of the whole heterogeneous bibliographic information network that completely represents the topology structure of academic activities. Then, a convolutional neural network model is designed to capture the complex nonlinear relationships between the early network features and the final cumulative citation count. Finally, we conduct an experiment on papers of Markov Chain from 1980 to 1985. The result shows that the prediction performance can be improved by 5% to baseline models under the same problem definition and with the same dataset. Meanwhile, the long-term scientific impacts are strongly correlated with its recognition by authoritative authors or venues in the early stage. INDEX TERMSScientific impact, heterogeneous bibliographic information network, convolutional neural network, citation count.

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Predicting citation counts based on deep neural network learning techniques

Cited by 110 publications

References 47 publications

The study of scientific map and thematic structure of social work

The study of scientific map and thematic structure of social work

A Neural Citation Count Prediction Model based on Peer Review Text

Early Prediction of Scientific Impact Based on Multi-Bibliographic Features and Convolutional Neural Network

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