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

TL;DR

This paper develops neural network models to accurately predict a journal's future citation count and CiteScore one year in advance, aiding stakeholders in assessing journal impact.

Contribution

It introduces a dataset of historical bibliometric data and compares neural network models with baselines, demonstrating improved prediction accuracy.

Findings

Neural networks outperform classical models in prediction accuracy.

Multi-Layer Perceptron and LSTM models are effective for bibliometric forecasting.

Proposed models provide reliable future performance estimates for journals.

Abstract

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.