arXiv 2210.129082022-10-24cs.DLcs.LG

提前一年预测期刊被引次数和CiteScore

Predicting the Citation Count and CiteScore of Journals One Year in Advance

Carleton University（卡尔顿大学）

William Croft, Jörg-Rüdiger Sack

摘要：

预测学术期刊的未来表现是一项可以使各种利益相关者受益的任务，包括编辑人员，出版商，索引服务，研究人员，大学管理人员和授予机构。利用期刊表现的历史数据，这可以被视为一个机器学习回归问题。在这项工作中，我们研究了两个这样的回归任务：1）预测期刊在下一个日历年将收到的引用数量，2）预测期刊在下一个日历年将被分配的Elsevier CiteScore。为了解决这些任务，我们首先创建一个数据集的历史文献计量学数据的期刊索引在Scopus。我们建议使用在我们的数据集上训练的神经网络模型来预测期刊的未来表现。为此，我们进行特征选择和模型配置的多层感知器和长短期记忆。通过与启发式预测基线和经典机器学习模型的实验比较，我们证明了我们提出的模型在预测未来引文和CiteScore值方面的优越性能。

英文摘要：

Prediction of the future performance of academic journals is a task that can benefit a variety of stakeholders including editorial staff, publishers, indexing services, researchers, university administrators and granting agencies. Using historical data on journal performance, this can be framed as a machine learning regression problem. In this work, we study two such regression tasks: 1) prediction of the number of citations a journal will receive during the next calendar year, and 2) prediction of the Elsevier CiteScore a journal will be assigned for the next calendar year. To address these tasks, we first create a dataset of historical bibliometric data for journals indexed in Scopus. We propose the use of neural network models trained on our dataset to predict the future performance of journals. To this end, we perform feature selection and model configuration for a Multi-Layer Perceptron and a Long Short-Term Memory. Through experimental comparisons to heuristic prediction baselines and classical machine learning models, we demonstrate superior performance in our proposed models for the prediction of future citation and CiteScore values.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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