Citation analysis on Google Scholar

• Main Author: Vidur Puliani
• Other Authors: Xiao Xiao Kui
• Format: Final Year Project
• Language: English
• Published: 2015
• Subjects: DRNTU::Engineering::Computer science and engineering::Mathematics of computing::Numerical analysis
• Online Access: http://hdl.handle.net/10356/62821
• Institution: Nanyang Technological University

Google Scholar, Scopus and Web of Science are some of the most commonly used online databases for scholarly work. The mentioned databases vary in their coverage and accuracy of citation counts. This purpose of this report is to conduct citation analysis on Google Scholar. H-index, among other statistics, is a widely used measure to evaluate the number of citations of an author. H-index is often used to evaluate the impact of an author’s work on his or her peers and used as an evaluation tool for grants and promotions. Given the importance of h-index as a measure, it is important to identify and extract any possible distortions to give an unbiased measure of an author’s influence. The total citation counts used to calculate the h-index includes self-citations. Self-citations are citations where the author of the citing paper and cited paper are the same. A higher number of self-citations might correlate with higher h-index, which does not necessarily imply a greater influence of an author’s work. Therefore, this report aims to analyse the effect of self-citation on h-index by calculating two h-index values, one using the total number of citations and the other excluding the self-citations. A python crawler was developed to collect the citation data for three authors from Google Scholar and store it in a local database for analysis. The citation analysis shows that the h-index value without self-citation decreases, albeit the effect was limited and non-uniform.