Can Scientific Impact Be Predicted?

TL;DR

This paper presents models to predict future h-indices of researchers and identify influential papers, demonstrating high accuracy and revealing key factors like topical authority and publication venue.

Contribution

It introduces a novel predictive model for authors' future h-indices and analyzes factors influencing paper impact, with an online tool for practical use.

Findings

High prediction accuracy for future h-indices (R2=0.92)

Effective identification of impactful papers (F1=0.99 for previous, 0.77 for new papers)

Topical authority and venue are key factors, topic popularity is not

Abstract

A widely used measure of scientific impact is citations. However, due to their heavy-tailed distribution, citations are fundamentally difficult to predict. Instead, to characterize scientific impact, we address two analogous questions asked by many scientific researchers: "How will my h-index evolve over time, and which of my previously or newly published papers will contribute to it?" To answer these questions, we perform two related tasks. First, we develop a model to predict authors' future h-indices based on their current scientific impact. Second, we examine the factors that drive papers---either previously or newly published---to increase their authors' predicted future h-indices. By leveraging relevant factors, we can predict an author's h-index in five years with an R2 value of 0.92 and whether a previously (newly) published paper will contribute to this future h-index with an F1 score of 0.99 (0.77). We find that topical authority and publication venue are crucial to these effective predictions, while topic popularity is surprisingly inconsequential. Further, we develop an online tool that allows users to generate informed h-index predictions. Our work demonstrates the predictability of scientific impact, and can help scholars to effectively leverage their position of "standing on the shoulders of giants."