Scientific impact evaluation and the effect of self-citations: mitigating the bias by discounting h-index

TL;DR

This paper introduces a new impact measure that discounts self-citations, addressing biases in traditional metrics like the h-index, and demonstrates its effectiveness through real-world evaluations of researchers and journals.

Contribution

It proposes a novel impact index that accounts for self-citations without prior distribution knowledge, improving impact assessment accuracy.

Findings

Self-citations significantly influence impact rankings.

The new measure reduces bias caused by self-citations.

It provides more accurate impact evaluations for researchers and journals.

Abstract

In this paper, we propose a measure to assess scientific impact that discounts self-citations and does not require any prior knowledge on the their distribution among publications. This index can be applied to both researchers and journals. In particular, we show that it fills the gap of h-index and similar measures that do not take into account the effect of self-citations for authors or journals impact evaluation. The paper provides with two real-world examples: in the former, we evaluate the research impact of the most productive scholars in Computer Science (according to DBLP); in the latter, we revisit the impact of the journals ranked in the 'Computer Science Applications' section of SCImago. We observe how self-citations, in many cases, affect the rankings obtained according to different measures (including h-index and ch-index), and show how the proposed measure mitigates this effect.