共查询到18条相似文献，搜索用时 203 毫秒
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在线科学交流中学术论文影响力动态评价研究 总被引：1，自引：1，他引：0
[目的/意义]构建在线科学交流中的学术论文影响力评价指标和模型,丰富学术论文影响力评价的内容和方法。[方法/过程]首先,分析目前学术评价研究中指标的特点,参考Altmetrics指标体系,选取国内几大权威在线媒体平台为研究平台,获取相关指标数据。其次,通过与被引频次和下载量的相关性筛选指标,运用主成分分析法构建评价模型。最后,以实证分析验证模型的有效性。[结果/结论]通过对学术论文在网络环境下的影响力研究及评价,改进传统学术论文评价模型的单一性和片面性,优化学术论文影响力评价的指标体系,为相关研究提供新思路和方法。 相似文献
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[目的/意义] 研究Altmetrics指标的主要特征及其与传统文献计量指标的相关性,以及它们随时间的演化情况;同时,基于Altmetrics指标全面评价学术论文的社会影响力和学术影响力,对于发展和完善Altmetrics计量系统至关重要。[方法/过程] 以2014-2016年Altmetric Top 100论文为样本,对每年的高Altmetrics指标论文的来源期刊、学科分布、获取方式、作者地域及研究机构分布进行统计分析,并讨论这些论文的社会影响力,同时对论文的Altmetric分数与其Web of Science上的被引频次进行相关性分析,研究相关性随时间的动态演化。[结果/结论] 研究结果表明,高Altmetrics指标论文主要来源于一些高影响因子期刊,其学科主要集中于医疗健康与生物科学,论文作者主要来自于欧美发达国家的高水平研究机构,且高Altmetrics指标论文中开放及自由获取的比例逐年增加;Altmetric分数能够定量地反映学术论文在社交和新闻媒体上被公众关注的程度,从而在一定程度上体现出学术论文的社会影响力;高Altmetrics指标论文的Altmetric分数与其被引频次存在一定正相关,表明高Altmetrics指标论文同时具有较高的学术影响力。 相似文献
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综合运用科技文献特征向量空间和机器学习模型实现海量文献中潜在“精品”的自动识别与推荐,能够提升海量科技文献的科学影响和其科技发展促进作用。设计和实现基于机器学习的科技文献潜在“精品”识别分类器和模型框架,测度出国际高影响力期刊和国内图书情报与档案管理期刊论文的原文及引文特征,运用特征工程构建科技论文特征向量空间;然后分别采用支持向量机和朴素贝叶斯等传统机器学习模型,以及深度置信网络和多层感知机等深度学习模型进行潜在“精品”的自动识别,并基于ROC曲线（receiver operating characteristic curve）和混淆矩阵构建评价模型识别效果的指标体系。研究结果显示：(1)深度学习模型在潜在“精品”识别方面的效果较差,而传统机器学习模型的识别效果较优,其中随机森林和支持向量机的潜在“精品”识别效果最佳,决策树识别效果次之,朴素贝叶斯识别效果较差且稳定性不足。(2)影响因子越高的期刊潜在“精品”识别效果越好;无论国际自然科学领域高影响力期刊,还是国内社会科学领域图书情报与档案管理期刊,识别出的“精品”论文全部为被引频次较高的论文且综述论文的占比较低,国内期刊的“精品”论... 相似文献
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从编辑的视点探讨科技期刊Altmetrics的重要性 总被引：1，自引：0，他引：1
单篇学术论文客观的定量指标评价越来越受到重视,Altmetrics有别于传统文献计量评价中的被引频次指标,是一种采用评价网络上学术论文影响力的非传统的新计量方法。文章从编辑的视点分析归纳了Altmetrics所具有的客观性,公正、透明性,覆盖、社会性,补充性,实时性特征。通过文献研究、整理,得到在社交媒体中Altmetrics即时测量出的学术论文的影响力,与科技期刊的学术影响力相吻合;在开放存取领域中,Altmetrics学术影响力计量方法已得到认识、关注和应用,并为科技期刊的影响力计量化、可视化评价提供依据。 相似文献
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[目的/意义]学术社交网络所提供的问答服务已成为学者们快速获取学术信息、解决学术问题的重要途径,实现基于机器学习的问答质量智能评价和服务优化对学术社交网络中优质内容传播具有重要意义。[方法/过程]以ResearchGate问答服务为研究对象,从结构化特征、内容特征、其他特征以及回答者特征4个维度构建答案质量评价体系,利用机器学习方法和数据增强技术进行答案质量分类预测。[结果/结论]SMOTE算法在处理不平衡样本时具备有效性;支持向量机在单一模型预测中,取得出色的分类效果;组合模型使预测精度得到进一步提升,基于随机森林、支持向量机、BP神经网络构建的组合模型分类性能最佳,以此为基础可通过搭建问答质量智能评价系统实现学术社交网络问答服务优化。 相似文献
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[目的/意义] 研究网络环境下大学生群体的信息偶遇敏感影响因素,以指导大学生群体提高信息偶遇能力,继而提升大学生信息素养。[方法/过程] 使用信息增益分析各影响因素与信息偶遇发生频次之间的相关性,构建敏感影响因素模型,并进一步利用支持向量机（SVM）建立信息偶遇频次预测模型。[结果/结论] 与发生信息偶遇最相关的10个影响因素分布于信息用户、偶遇信息、网络环境、情境因素4个维度;模型分类预测精度达82.96%,说明SVM对预测信息偶遇频次有良好效果。 相似文献
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[目的/意义]识别学术论文同行评审意见类型、分析不同被引频次下同行评审意见类型在同行评审报告中的分布情况,有助于加深对同行评议机制的认识,为评估论文学术质量、量化评审专家贡献提供新思路。[方法/过程]首先,将同行评审意见类型划分为正面评价、负面评价、要求/建议(主、次要方面)、问题/疑问、陈述六个类别,经人工标注、获取训练、测试语料后,对比分析传统机器学习模型、深度学习模型在同行评审意见类型自动识别上的效果;其次,将同行评审报告涉及的学术论文进行主题聚类,进而对被引频次进行标准化处理;最后,使用Spearman相关系数、累积分布、K-S检验、负二项回归分析不同被引频次学术论文对应的同行评审报告中同行评审意见类型的分布情况。[结果/结论]SciBert模型识别效果最佳;在基于Spearman的相关性分析中,评审报告中正面评价的分布占比与被引频次具有显著的弱正相关,负面评价的分布占比与被引频次具有显著的弱负相关;通过累计分布发现,多数情况下,当累积概率相同时,高被引分区中正面评价的分布占比大于低被引分区、负面评价的分布占比小于低被引分区,K-S检验能够检测到这种差异;在负二项回归分析中,正面评价分布占比、负面评价分布占比分别对被引频次有显著的正向影响、负向影响。研究结果表明,同行评审报告中正面评价、负面评价的分布情况与其对应论文的被引频次存在相关性,被引频次一定程度上能够反映论文的学术质量。 相似文献
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[目的/意义]研究时间因素对专利被引频次的影响,可以减少时间因素对技术评价活动的制约,提高评价的准确性和可信度。[方法/过程]采集1975-2017年的美国专利数据,开展基于固定效应法的专利被引频次的修正研究。将专利按照不同公开年份和不同技术领域分组,选定组内均值和6个TOP分位数为被引频次基准,统计当前时间点的被引频次基准线及基准线的历史时序变化情况。建立神经网络模型,拟合基准线的时序变化规律,并预测未来统计时间点的基准线。[结果/结论]专利公开年份和统计年份的时间差异,使得专利被引频次无法直接进行比较。本文建立了基于不同技术领域、不同公开年份和不同统计年份的专利被引频次基准线,为专利评估提供参考。 相似文献
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数据完备性是基于数据驱动的分析方法的一个重要的前提,不完备的数据意味着很可能会丢失重要的判别信息,从而影响分析结果的准确性.针对现实情况下客户数据特征不同以及数据的不完备性对传统的基于数据驱动的分析方法的不利影响,本文将机器学习领域中迁移分类的方法应用于客户流失预测,通过谱特征排列(spectral feature alignment,SFA)实现了跨领域属性的近似统一,并利用直推式支持向量机(transductive support vector machines,TSVM)对客户数据进行分类从而识别忠诚客户和流失客户,使得预测模型的性能显著提高.在最后的实验部分,使用两个不同的数据集进行的迁移分类结果证明了本文提出模型的有效性. 相似文献
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个性化服务中基于支持向量机的用户兴趣挖掘分析 总被引：2，自引：1，他引：1
用户兴趣的准确获取是保证个性化服务质量的关键;分析用户所浏览的网页集,捕获用户兴趣、建立用户兴趣挖掘模型是目前个性化服务研究的一个重要方向.本文首先比较支持向量机与其他分类算法的性能,分析了基于支持向量机进行用户兴趣挖掘的可行性;然后在介绍支持向量机分类原理和分析分类预测理论模型的基础上构建了用户兴趣挖掘模型;最后选取中文网页分类训练集和特定用户的网页浏览记录,主要从支持向量机分类器的构造和用户兴趣预测两个方面进行了模型的实现研究. 相似文献
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��[Purpose/significance] This paper summarizes the influencing factors and prediction methods of academic paper citation, analyzes the existing problems and proposes the future development directions.[Method/process] This paper used the literature research method to review the research progress of academic papers at home and abroad, and summarized the relevant content and characteristics of influencing factors and prediction methods.[Result/conclusion] There are many indicators of influencing factors, but there is no unified selection criteria. The theoretical basis of prediction methods is weak. The research on dynamics of citation prediction is insufficient. The generality of prediction models is limited. In the future, we should strengthen the theoretical research of citation prediction methods, the combination of traditional bibliometrics and alternative metrics, the deep application of natural language processing, and establish a unified baseline standard, a more accurate prediction model. 相似文献
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《Journal of Informetrics》2020,14(3):101039
Predicting the citation counts of academic papers is of considerable significance to scientific evaluation. This study used a four-layer Back Propagation (BP) neural network model to predict the five-year citations of 49,834 papers in the library, information and documentation field indexed by the CSSCI database and published from 2000 to 2013. We extracted six paper features, two journal features, nine author features, eight reference features, and five early citation features to make the prediction. The empirical experiments showed that the performance of the BP neural network is significantly better than those of the six baseline models. In terms of the prediction effect, the accuracy of the model at predicting infrequently cited papers was higher than that for frequently cited ones. We determined that five essential features have significant effects on the prediction performance of the model, i.e., ‘citations in the first two years’, ‘first-cited age’, ‘paper length’, ‘month of publication’, and ‘self-citations of journals’, and the other features contribute only slightly to the prediction. 相似文献
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运用引文增强技术对《档案管理》的发文和被引数据进行深层关联分析,从施引学科、施引群体、施引机构、学术宣传效果等角度探讨该刊的学术影响力特征及影响因素。本研究发现该刊的学科影响力主要局限在档案学领域,施引机构多集中在高校,该刊的论文价值发现周期偏长,被错引的比例较高,且高频施引者占比较少,建议该刊扩大选题范围,积极吸纳亲缘学科的相关论文;奖励活跃贡献者,提高作者回报率;重视学术宣传工作,降低被误引的概率,从而进一步提升学术影响力。 相似文献
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《Journal of Informetrics》2022,16(1):101235
With the advancement of science and technology, the number of academic papers published each year has increased almost exponentially. While a large number of research papers highlight the prosperity of science and technology, they also give rise to some problems. As we know, academic papers are the most intuitive embodiment of the research results of scholars, which can reflect the level of researchers. It is also the standard for evaluation and decision-making of them, such as promotion and allocation of funds. Therefore, how to measure the quality of an academic paper is very critical. The most common standard for measuring the quality of academic papers is the number of citation counts of them, as this indicator is widely used in the evaluation of scientific publications. It also serves as the basis for many other indicators (such as the h-index). Therefore, it is very important to be able to accurately predict the citation counts of academic papers. To improve the effective of citation counts prediction, we try to solve the citation counts prediction problem from the perspective of information cascade prediction and take advantage of deep learning techniques. Thus, we propose an end-to-end deep learning framework (DeepCCP), consisting of graph structure representation and recurrent neural network modules. DeepCCP directly uses the citation network formed in the early stage of the paper as the input, and outputs the citation counts of the corresponding paper after a period of time. It only exploits the structure and temporal information of the citation network, and does not require other additional information. According to experiments on two real academic citation datasets, DeepCCP is shown superior to the state-of-the-art methods in terms of the accuracy of citation count prediction. 相似文献
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《Journal of Informetrics》2019,13(2):485-499
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. 相似文献
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《Journal of Informetrics》2023,17(3):101413
Understanding paper citation dynamics and accurately predicting future citation counts of papers is of significant interest, and thus modeling citation dynamics as an information cascade has recently attracted considerable attention. Nevertheless, most of these recent deep learning-based information cascade prediction models are focused on the embedding of each individual node rather than the entire structure of the cascade graph, which limits the robustness of the model. Thus, instead of learning the representation of each node in the cascade, we propose learning the dynamic structural representation of the entire information cascade graph with the degree distribution vectors corresponding to different timestamps as the input of a sequential deep neural network, named CasDENN. Extensive experiments on datasets from academic paper citations (APS) and social media post forwards (Weibo) show a dramatic improvement over state-of-the-art baselines, where the prediction error can be reduced by approximately 8%–10% and the running time is less than 10% of the fast baseline. 相似文献