| 基于Altmetrics视角的学术论文被引频次影响因素分析和预测 |
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| 引用本文： | 段丹,梁柏静,于文文,孙昕,张璐.基于Altmetrics视角的学术论文被引频次影响因素分析和预测[J].图书馆杂志,2020(4):102-112. |
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| 作者姓名： | 段丹 梁柏静 于文文 孙昕 张璐 |
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| 作者单位： | 东北财经大学图书馆;东北财经大学管理科学与工程学院 |
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| 基金项目： | 2019年辽宁省高校图书情报委员会基金项目“基于Altmetrics视角的学术论文影响力分析和引用预测”(项目编号:LTB201911);2019年辽宁省教育厅青年科技人才“育苗”项目“基于机器学习模型的学术论文影响力预测研究”(项目编号:LN2019Q11)的研究成果之一 |
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| 摘 要： | 本文创新性构建学术论文被引影响因素特征空间,以我校SCI&SSCI学术论文为例,验证机器学习模型在预测学术论文被引频次研究中的有效性和准确性,本文的分析结论可以为高校图书馆开展决策支持服务提供参考。本文梳理学术论文被引频次影响因素及预测方法的相关研究,结合传统文献计量和Altmetrics指标构建学术论文影响因素的特征空间,并通过实验比较线性回归、神经网络、支持向量机三种机器学习模型在预测学术论文被引频次研究中的有效性和准确性。本文的分析结论证明基于Altmetrics视角构建的特征空间的预测准确率大幅度提高,并且支持向量机模型在对学术论文影响力预测的实证研究中表现出优异的性能。
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| 关 键 词： | Altmetrics 学术论文被引频次 影响因素 机器学习 |
Analysis of Influencing Factors on Citation Frequency of Academic Papers Based on Altermetrics Perspective and Prediction |
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| Duan Dan,Liang Baijing,Yu Wenwen,Sun Xin,Zhang Lu.Analysis of Influencing Factors on Citation Frequency of Academic Papers Based on Altermetrics Perspective and Prediction[J].Library Journal,2020(4):102-112. |
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| Authors: | Duan Dan Liang Baijing Yu Wenwen Sun Xin Zhang Lu |
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| Institution: | (Dongbei University of Finance&Economics Library;School of Mainagement Science and Engineering,Dongbei University of Finance&Economics) |
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| Abstract: | This paper innovatively constructs the prediction model of influencing factors on citation of academic papers.Taking SCI&SSCI scientific research papers of our university as an example,it verifies the validity and accuracy of machine learning model in predicting citation frequency of academic papers.The conclusions of this paper can provide reference for university libraries to carry out decision support services.This paper combs the related literature of influencing factors and prediction methods of cited frequency of academic papers,constructs the characteristic space of influencing factors of academic papers by combining traditional literature measurement and Altmetrics index,and compares the effectiveness and accuracy of three kinds of machine learning models of linear regression,neural network and support vector machine in predicting cited frequency of academic papers through experiments.The conclusion of this paper proves that the prediction accuracy of feature space constructed from the perspective of altmetrics is greatly improved,and that the support vector machine model shows excellent performance in the empirical research of the impact prediction of academic papers. |
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| Keywords: | Altmetrics Citation frequency of academic papers Influencing factors Machine learing |
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