h-Index Manipulation by Undoing Merges

TL;DR

This paper investigates how researchers can manipulate their h-index by undoing article merges in Google Scholar, revealing computational complexities and demonstrating that small h-index improvements are easily achievable through such splits.

Contribution

It provides a comprehensive analysis of the computational complexity of h-index manipulation via undoing merges and empirically shows the ease of small h-index improvements.

Findings

Manipulation by splitting articles can be achieved with small effort.

The problem exhibits various computational complexities, from linear algorithms to hardness results.

Small h-index improvements are practically easy to obtain.

Abstract

The h-index is an important bibliographic measure used to assess the performance of researchers. Dutiful researchers merge different versions of their articles in their Google Scholar profile even though this can decrease their h-index. In this article, we study the manipulation of the h-index by undoing such merges. In contrast to manipulation by merging articles (van Bevern et al. [Artif. Intel. 240:19-35, 2016]) such manipulation is harder to detect. We present numerous results on computational complexity (from linear-time algorithms to parameterized computational hardness results) and empirically indicate that at least small improvements of the h-index by splitting merged articles are unfortunately easily achievable.