The skewness of computer science

TL;DR

This paper analyzes the citation distribution in computer science, revealing a highly skewed pattern with power law tails, especially pronounced in conference publications, and highlights the dominance of journal papers in impact metrics.

Contribution

It provides a detailed analysis of citation skewness in computer science, emphasizing differences between conference and journal publication impacts.

Findings

Citation distribution is highly skewed with a power law tail.

Conference publications exhibit more pronounced skewness than journal papers.

Journal papers have a greater impact as measured by bibliometric indicators.

Abstract

Computer science is a relatively young discipline combining science, engineering, and mathematics. The main flavors of computer science research involve the theoretical development of conceptual models for the different aspects of computing and the more applicative building of software artifacts and assessment of their properties. In the computer science publication culture, conferences are an important vehicle to quickly move ideas, and journals often publish deeper versions of papers already presented at conferences. These peculiarities of the discipline make computer science an original research field within the sciences, and, therefore, the assessment of classical bibliometric laws is particularly important for this field. In this paper, we study the skewness of the distribution of citations to papers published in computer science publication venues (journals and conferences). We find that the skewness in the distribution of mean citedness of different venues combines with the asymmetry in citedness of articles in each venue, resulting in a highly asymmetric citation distribution with a power law tail. Furthermore, the skewness of conference publications is more pronounced than the asymmetry of journal papers. Finally, the impact of journal papers, as measured with bibliometric indicators, largely dominates that of proceeding papers.