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Purpose — The chapter presents the practical applications of web search statistics analysis. The process description highlights the potential use of search queries and statistical data and how they could be used in various forecasting situations. The presented case is an example of applied computational intelligence and the main focus is oriented towards the decision support offered by the software mechanism and its capabilities to automatically gather, process and analyse data.

Methodology/approach — The statistics of the search queries as a source of prognostic information are analysed in a step-by-step process, starting from their content and scope, their processing and applications, and concluding with usage in a software-based intelligent framework.

Research implications — The analysis of search engine trends offers a great opportunity for many areas of research. Into the future, deploying this information in the prognosis will further develop intelligent data processing.

Practical implications — This functionality offers a unique possibility, impossible until now, to observe, estimate and predict various processes using wide, precise and accurate behaviour observations. The scope and quality of data allow practitioners to successfully use it in various prognostic problems (i.e. political, medical, or economic).

Originality/value of paper — The chapter presents practical implications of technology. The chapter then highlights potential areas that would benefit from the analysis of queries statistics. Moreover, it introduces ‘WebPerceiver’, an intelligent platform, built to make the analysis and usage of search trends easier and more generally available to a wide audience, including non-skilled users.

The study aims to analyze the synergy of artificial intelligence (AI), with scientometrics, webometrics and bibliometrics to unlock and to emphasize the potential of the applications and benefits of AI algorithms in these fields.

By conducting a systematic literature review, our aim is to explore the potential of AI in revolutionizing the methods used to measure and analyze scholarly communication, identify emerging research trends and evaluate the impact of scientific publications. To achieve this, we implemented a comprehensive search strategy across reputable databases such as ProQuest, IEEE Explore, EBSCO, Web of Science and Scopus. Our search encompassed articles published from January 1, 2000, to September 2022, resulting in a thorough review of 61 relevant articles.

(1) Regarding scientometrics, the application of AI yields various distinct advantages, such as conducting analyses of publications, citations, research impact prediction, collaboration, research trend analysis and knowledge mapping, in a more objective and reliable framework. (2) In terms of webometrics, AI algorithms are able to enhance web crawling and data collection, web link analysis, web content analysis, social media analysis, web impact analysis and recommender systems. (3) Moreover, automation of data collection, analysis of citations, disambiguation of authors, analysis of co-authorship networks, assessment of research impact, text mining and recommender systems are considered as the potential of AI integration in the field of bibliometrics.

This study covers the particularly new benefits and potential of AI-enhanced scientometrics, webometrics and bibliometrics to highlight the significant prospects of the synergy of this integration through AI.

This paper seeks to discuss measurement units by comparing the internet use and the traditional media use, and to understand internet use from the traditional media use perspective.

Benefits and shortcomings of two log file types will be carefully and exhaustively examined. Client‐side and server‐side log files will be analyzed and compared with proposed units of analysis.

Server‐side session time calculation was remarkably reliable and valid based on the high correlation with the client‐side time calculation. The analysis result revealed that the server‐side log file session time measurement seems more promising than the researchers previously speculated.

An ability to identify each individual user and low caching problems were strong advantages for the analysis. Those web design implementations and web log data analysis scheme are recommended for future web log analysis research.

This paper examined the validity of the client‐side and the server‐side web log data. As a result of the triangulation of two datasets, research designs and propose analysis schemes could be recommended.

Along with the ever growingWeb is the proliferation of objectionable content, such as sex, violence, racism, etc. We need efficient tools for classifying and filtering undesirable web content. In this paper, we investigate this problem through WebGuard, our automatic machine learning based pornographic website classification and filtering system. Facing the Internet more and more visual and multimedia as exemplified by pornographic websites, we focus here our attention on the use of skin color related visual content based analysis along with textual and structural content based analysis for improving pornographic website filtering. While the most commercial filtering products on the marketplace are mainly based on textual content‐based analysis such as indicative keywords detection or manually collected black list checking, the originality of our work resides on the addition of structural and visual content‐based analysis to the classical textual content‐based analysis along with several major‐data mining techniques for learning and classifying. Experimented on a testbed of 400 websites including 200 adult sites and 200 non pornographic ones, WebGuard, our Web filtering engine scored a 96.1% classification accuracy rate when only textual and structural content based analysis are used, and 97.4% classification accuracy rate when skin color related visual content based analysis is driven in addition. Further experiments on a black list of 12 311 adult websites manually collected and classified by the French Ministry of Education showed that WebGuard scored 87.82% classification accuracy rate when using only textual and structural content‐based analysis, and 95.62% classification accuracy rate when the visual content‐based analysis is driven in addition. The basic framework of WebGuard can apply to other categorization problems of websites which combine, as most of them do today, textual and visual content.

Web sites are typically designed attending to a variety of criteria. However, web site structure determines browsing behavior and way‐finding results. The aim of this study is to identify the main profiles of web sites' organizational structure by modeling them as graphs and considering several social network analysis features.

A case study based on 80 institutional Spanish universities' web sites has been used for this purpose. For each root domain, two different networks have been considered: the first is the domain network, and the second is the page network. In both cases, several indicators related to social network analysis have been evaluated to characterize the web site structure. Factor analysis provides the statistical methodology to adequately extract the main web site profiles in terms of their internal structure.

This paper allows the categorization of web site design styles and provides general guidelines to assist designers to better identify areas for creating and improving institutional web sites. The findings of this study offer practical implications to web site designers for creating and maintaining an effective web presence, and for improving usability.

The research is limited to 80 institutional Spanish universities' web sites. Other institutional university web sites from different countries can be analyzed, and the conclusions could be compared or enlarged.

This paper highlights the importance of the internal web sites structure, and their implications on usability and way‐finding results. As a difference to previous research, the paper is focused on the comparison of internal structure of institutional web sites, rather than analyzing the web as a whole or the interrelations among web sites.

The purpose of this paper is to explore the use of LexiURL as a Web intelligence tool for collecting and analysing links to digital libraries, focusing specifically on the National electronic Library for Health (NeLH).

The Web intelligence techniques in this study are a combination of link analysis (web structure mining), web server log file analysis (web usage mining), and text analysis (web content mining), utilizing the power of commercial search engines and drawing upon the information science fields of bibliometrics and webometrics. LexiURL is a computer program designed to calculate summary statistics for lists of links or URLs. Its output is a series of standard reports, for example listing and counting all of the different domain names in the data.

Link data, when analysed together with user transaction log files (i.e. Web referring domains) can provide insights into who is using a digital library and when, and who could be using the digital library if they are “surfing” a particular part of the Web; in this case any site that is linked to or colinked with the NeLH. This study found that the NeLH was embedded in a multifaceted Web context, including many governmental, educational, commercial and organisational sites, with the most interesting being sites from the.edu domain, representing American Universities. Not many links directed to the NeLH were followed on September 25, 2005 (the date of the log file analysis and link extraction analysis), which means that users who access the digital library have been arriving at the site via only a few select links, bookmarks and search engine searches, or non‐electronic sources.

A number of studies concerning digital library users have been carried out using log file analysis as a research tool. Log files focus on real‐time user transactions; while LexiURL can be used to extract links and colinks associated with a digital library's growing Web network. This Web network is not recognized often enough, and can be a useful indication of where potential users are surfing, even if they have not yet specifically visited the NeLH site.

With the proliferation of terrorist/extremist websites on the World Wide Web, it has become progressively more crucial to detect and analyze the content on these websites. Accordingly, the volume of previous research focused on identifying the techniques and activities of terrorist/extremist groups, as revealed by their sites on the so-called dark web, has also grown.

This study presents a review of the techniques used to detect and process the content of terrorist/extremist sites on the dark web. Forty of the most relevant data sources were examined, and various techniques were identified among them.

Based on this review, it was found that methods of feature selection and feature extraction can be used as topic modeling with content analysis and text clustering.

At the end of the review, present the current state-of-the- art and certain open issues associated with Arabic dark Web content analysis.

This article introduces the application of informetric methods to the World Wide Web (WWW), also called Webometrics. A case study presents a workable method for general informetric analyses of the WWW. In detail, the paper describes a number of specific informetric analysis parameters. As a case study the Danish proportion of the WWW is compared to those of other Nordic countries. The methodological approach is comparable with common bibliometric analyses of the ISI citation databases. Among other results the analyses demonstrate that Denmark would seem to fall seriously behind the other Nordic countries with respect to visibility on the Net and compared to its position in scientific databases.

The purpose of this paper is to explore webometric analysis of keywords and expressions of the biochemistry field of study via LexiURL Searcher.

Interfaces for assisting users with information access have received considerable attention. Along with the extraction of data on Web sites for webometric purposes (e.g. link analysis, ranking of Web sites, etc.), LexiURL Searcher presents some information on the arrangement of links among different Web sites. Such capability enables users to identify one or more Web sites around their intended subject and, accordingly, explore all Web sites linked with their identified Web site(s). LexiURL Searcher has preceded webometric analysis by considering the main expressions and keywords derived from the MeSH database.

The worldwide survey indicated that links from countries such as England, Japan, Germany, Australia and Canada were among the Web sites that are most used in biochemistry. Alternatively, other countries such as Singapore, Thailand and Poland had the most advantageous links to the outside world, whereas South Africa, New Zealand and The Netherlands had the least link effect. Biochemistry, being a specialized domain, would benefit greatly from site linking and would provide users the most assistance in information processing.

Most webometric studies remain on the level of link analysis and Web site statuses; however, this paper gives information on the common thread Web sites based on a standard thesaurus.

Internet usage has exploded over the past decade and the medium is now being suggested as a potentially powerful tool for disclosing environmental information and increasing corporate accountability. This study, grounded in legitimacy theory, argues that such a view may be overly optimistic. Results of an analysis of both annual report and corporate web page environmental disclosures for a sample of 62 U.S. firms do indicate that corporate web pages appear to be adding at least some additional, non-redundant environmental information beyond what is provided in the annual reports. However, the relative lack of negative environmental disclosure on the web pages, in conjunction with the finding that differences in the level of positive/neutral environmental disclosure are associated with legitimacy variables suggests that the focus of Internet disclosure may be more on corporate attempts at legitimation than on moving toward greater corporate accountability.