论文标题
预测引用数量并提前一年
Predicting the Citation Count and CiteScore of Journals One Year in Advance
论文作者
论文摘要
对学术期刊未来表现的预测是一项可以使包括编辑人员，出版商，索引服务，研究人员，大学管理人员和授予机构在内的各种利益相关者受益的任务。使用有关期刊性能的历史数据，可以将其作为机器学习回归问题进行框架。在这项工作中，我们研究了两个这样的回归任务：1）预测下一个日历年期刊将收到的引用数量，以及2）预测Elsevier CitesCore将在下一个日历年中分配期刊。为了解决这些任务，我们首先为Scopus索引的期刊创建历史文献计量数据数据集。我们建议使用在数据集中训练的神经网络模型，以预测期刊的未来性能。为此，我们为多层感知器和较长的短期内存执行功能选择和模型配置。通过与启发式预测基线和经典机器学习模型的实验比较，我们在提出的模型中证明了未来引用和CitesCore值的卓越性能。
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.
