Journal of Data and Information Science ›› 2020, Vol. 5 ›› Issue (4): 43-55.doi: 10.2478/jdis-2020-0040

• Research Paper • Previous Articles     Next Articles

Exploring the Potentialities of Automatic Extraction of University Webometric Information

Gianpiero Bianchi1, Renato Bruni2, Cinzia Daraio2,(), Antonio Laureti Palma1, Giulio Perani1, Francesco Scalfati1   

  1. 1ISTAT, Italian National Institute of Statistics, Via Cesare Balbo 16, Rome 00184, Italy
    2DIAG, Sapienza University of Rome, Via Ariosto 25, Rome 00185, Italy
  • Received:2020-07-20 Revised:2020-10-28 Accepted:2020-11-09 Online:2020-09-20 Published:2020-11-20
  • Contact: Cinzia Daraio


Purpose: The main objective of this work is to show the potentialities of recently developed approaches for automatic knowledge extraction directly from the universities’ websites. The information automatically extracted can be potentially updated with a frequency higher than once per year, and be safe from manipulations or misinterpretations. Moreover, this approach allows us flexibility in collecting indicators about the efficiency of universities’ websites and their effectiveness in disseminating key contents. These new indicators can complement traditional indicators of scientific research (e.g. number of articles and number of citations) and teaching (e.g. number of students and graduates) by introducing further dimensions to allow new insights for “profiling” the analyzed universities.

Design/methodology/approach: Webometrics relies on web mining methods and techniques to perform quantitative analyses of the web. This study implements an advanced application of the webometric approach, exploiting all the three categories of web mining: web content mining; web structure mining; web usage mining. The information to compute our indicators has been extracted from the universities’ websites by using web scraping and text mining techniques. The scraped information has been stored in a NoSQL DB according to a semi-structured form to allow for retrieving information efficiently by text mining techniques. This provides increased flexibility in the design of new indicators, opening the door to new types of analyses. Some data have also been collected by means of batch interrogations of search engines (Bing, or from a leading provider of Web analytics (SimilarWeb, The information extracted from the Web has been combined with the University structural information taken from the European Tertiary Education Register (, a database collecting information on Higher Education Institutions (HEIs) at European level. All the above was used to perform a clusterization of 79 Italian universities based on structural and digital indicators.

Findings: The main findings of this study concern the evaluation of the potential in digitalization of universities, in particular by presenting techniques for the automatic extraction of information from the web to build indicators of quality and impact of universities’ websites. These indicators can complement traditional indicators and can be used to identify groups of universities with common features using clustering techniques working with the above indicators.

Research limitations: The results reported in this study refers to Italian universities only, but the approach could be extended to other university systems abroad.

Practical implications: The approach proposed in this study and its illustration on Italian universities show the usefulness of recently introduced automatic data extraction and web scraping approaches and its practical relevance for characterizing and profiling the activities of universities on the basis of their websites. The approach could be applied to other university systems.

Originality/value: This work applies for the first time to university websites some recently introduced techniques for automatic knowledge extraction based on web scraping, optical character recognition and nontrivial text mining operations (Bruni & Bianchi, 2020).

Key words: Development of data and information services, Webometrics indicators, Higher education institutions, Automatic extraction, Machine learning, Optimization