Predicting citation counts based on deep neural network learning techniques

TL;DR

This paper introduces a deep neural network approach to predict long-term citation counts of scientific papers based on early citation data, outperforming existing methods in accuracy.

Contribution

The paper presents a novel neural network-based model for citation prediction that improves accuracy over previous approaches.

Findings

Neural network model outperforms state-of-the-art methods.

Accurate long-term citation prediction from early citation data.

Effective for yearly and total citation forecasts.

Abstract

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 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 citations prediction model, we employed artificial neural networks 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 out-performs state-of-the-art methods with respect to the prediction accuracy in both yearly and total prediction of the number of citations.