Quantifying the topic disparity of scientific articles

TL;DR

This paper introduces a measure called topic disparity to quantify how much a scientific article's topic deviates from its discipline, revealing that less conventional topics tend to receive fewer citations, and offering a way to identify peripheral research.

Contribution

The study proposes a novel measure of topic disparity using neural embeddings and demonstrates its negative correlation with citation percentile across disciplines, controlling for various factors.

Findings

Less conventional research receives fewer citations.

Topic disparity negatively correlates with citation percentile.

Method can identify peripheral but valuable research.

Abstract

Citation count is a popular index for assessing scientific papers. However, it depends on not only the quality of a paper but also various factors, such as conventionality, team size, and gender. Here, we examine the extent to which the conventionality of a paper is related to its citation percentile in a discipline by using our measure, topic disparity. The topic disparity is the cosine distance between a paper and its discipline on a neural embedding space. Using this measure, we show that the topic disparity is negatively associated with the citation percentile in many disciplines, even after controlling team size and the genders of the first and last authors. This result indicates that less conventional research tends to receive fewer citations than conventional research. Our proposed method can be used to complement the raw citation counts and to recommend papers at the periphery of a discipline because of their less conventional topics.