Simplifying Impact Prediction for Scientific Articles

TL;DR

This paper proposes a simplified classification approach to predict the impact of scientific articles, reducing reliance on extensive metadata and focusing on practical, accessible impact estimation methods.

Contribution

It introduces a new impact prediction model that simplifies the problem to classification and requires minimal article metadata, making impact estimation more accessible.

Findings

Effective classification of articles by impact level.

Reduced need for rich metadata in impact prediction.

Comparable performance to more complex models.

Abstract

Estimating the expected impact of an article is valuable for various applications (e.g., article/cooperator recommendation). Most existing approaches attempt to predict the exact number of citations each article will receive in the near future, however this is a difficult regression analysis problem. Moreover, most approaches rely on the existence of rich metadata for each article, a requirement that cannot be adequately fulfilled for a large number of them. In this work, we take advantage of the fact that solving a simpler machine learning problem, that of classifying articles based on their expected impact, is adequate for many real world applications and we propose a simplified model that can be trained using minimal article metadata. Finally, we examine various configurations of this model and evaluate their effectiveness in solving the aforementioned classification problem.