Quantitive evaluation of Web site content and structure

TLDR

The study proposes an automated method to classify and evaluate publicly accessible Web sites. The authors develop a model‑building and validation framework that defines key attributes, creates operational criteria, employs software to collect data, and applies a multi‑method approach of clustering, textual analysis, supervised and unsupervised neural networks, and manual validation. The method proves useful for analyzing content and hypertext structures across commercial, educational, and non‑profit Web sites.

Abstract

Abstract Describes an approach automatically to classify and evaluate publicly accessible World Wide Web sites. The suggested methodology is equally valuable for analyzing content and hypertext structures of commercial, educational and non‐profit organizations. Outlines a research methodology for model building and validation and defines the most relevant attributes of such a process. A set of operational criteria for classifying Web sites is developed. The introduced software tool supports the automated gathering of these parameters, and thereby assures the necessary "critical mass" of empirical data. Based on the preprocessed information, a multi‐methodological approach is chosen that comprises statistical clustering, textual analysis, supervised and non‐supervised neural networks and manual classification for validation purposes.