Quantitive evaluation of Web site content and structure

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.

Diffusion and success factors of mobile marketing

“…Previous research has built a foundation for analyzing the structure and content of large samples of Web-based information systems [28,29]. Textual data gathered from Web sites contain valuable information about industry trends and competitive strategies.…”

“…As a rule of thumb, ten megabytes of visible and invisible text yielded three to five megabytes of plain text. The 10 megabyte limit helps manage available storage space and compare sites of heterogeneous size [28,32].…”

Index for comparative assessment of municipal websites

“…This weighted MWQAI score is the resultant evaluation index, i.e., the m-score for that city. Such a subjective value allocation process has been employed in the previous studies of Olsina et al (1999), Bauer and Scharl (2000), Buenadicha et al (2001), Miranda and Bañegil (2004), Esteves (2005), Miranda et al (2006Miranda et al ( , 2010 and Banerjee and Katare (2016).…”

Tracking and modelling information diffusion across interactive online media

“…Automatically gathering data from electronic media, by contrast, provide scalability, speed, consistency and abundant longitudinal data. Automated approaches alleviate methodological limitations of subjective impressions and anecdotal evidence (Bauer & Scharl, 2000;Scharl, 2000). Although Web crawlers cannot replace human evaluation, they handle dynamic data more efficiently and help avoid inter-and intra-personal variances.…”