Academic Ranking with Web Mining and Axiomatic Analysis

TL;DR

This paper introduces a novel methodology combining web mining and axiomatic analysis to produce rigorous academic rankings, demonstrated through computer science departments using Microsoft Academic Search data.

Contribution

It presents the first rigorous, axiomatic approach to academic ranking using web-mined bibliometric data, enabling automatic and comprehensive institutional evaluations.

Findings

First axiomatic ranking of computer science departments

Feasibility demonstrated with Microsoft Academic Search data

Potential for fully automatic university rankings

Abstract

Academic ranking is a public topic, such as for universities, colleges, or departments, which has significant educational, administrative and social effects. Popular ranking systems include the US News & World Report (USNWR), the Academic Ranking of World Universities (ARWU), and others. The most popular observables for such ranking are academic publications and their citations. However, a rigorous, quantitative and thorough methodology has been missing for this purpose. With modern web technology and axiomatic bibliometric analysis, here we perform a feasibility study on Microsoft Academic Search metadata and obtain the first-of-its-kind ranking results for American departments of computer science. This approach can be extended for fully automatic intuitional and college ranking based on comprehensive data on Internet.