Using Google Scholar to predict self citation: A case study in Health Economics

TL;DR

This study investigates how self citation influences academic metrics like the h index in Health Economics, using Google Scholar data and software to identify self citations among 545 researchers, revealing regional and career-stage patterns.

Contribution

It introduces a method to predict self citation using Google Scholar data and software, highlighting its impact on influence metrics in a specific academic field.

Findings

Self citation varies by region and career length.

Early career researchers self cite more frequently.

Researchers from Europe and Australasia have higher self citation rates.

Abstract

Metrics designed to quantify the influence of academics are increasingly used and easily estimable, and perhaps the most popular is the h index. Metrics such as this are however potentially impacted through excessive self citation. This work explores the issue using a group of researchers working in a well defined sub field of economics, namely Health Economics. It then employs self citation identification software, and identifies the characteristics that best predict self citation. This provides evidence regarding the scale of self citation in the field, and the degree to which self citation impacts on inferences about the relative influence of individual Health Economists. Using data from 545 Health Economists, it suggests self citation to be associated with the geographical region and longevity of the Health Economist, with early career researchers and researchers from mainland Europe and Australasia self citing most frequently.