Search results

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.

Purpose — Ranking is a natural task for a search engine; a search engine result page is the most common example. This chapter aims at illustrating the motivations and the concepts of rank correlation in a practical way for the researchers active in the different domains of search engines.

Methodology/approach — To this end, this chapter provides a survey according to a topic-oriented basis of the search engine evaluation literature specifically devoted to or based on rank correlation; the chapter explains and illustrates how statistics is the only approach to rank correlation.

Findings/research limitations/implications — The chapter introduces the pros and cons of rank correlation measures through a light-weight formal description and a number of concrete examples to find the measure that better fit a context.

Practical implications — This chapter provides a blueprint for the application of rank correlation within scientific experimentation or item/service recommendation.

Social implications — Rank correlation analyses impact on the success or failure of a search engine in performing the tasks for which it has been designed and hence on the people's daily life activities.

Originality/value of paper — This chapter places rank correlation within a scientific research perspective and in particular connects to and complements documentation on search engine evaluation.

This study seeks to focus on the following: discovering consumer preferences regarding package tours to China; revealing differences among consumers' cognition related to these package tours, travel advertisements on web sites and search engine results; identifying the similarities among travel agency web sites; and establishing a consumer cognitive structure to assist marketers in designing written content for display in search engine results.

The study adopted means‐end chain theory as a theoretical basis and used the written content of tourism web sites displayed in search engine results as an empirical object. By comparing the contents of tourism web sites and the search engine results, the researchers could analyse and compare similarities and differences among web site content, search results and consumer cognition.

Using the utility score of each attribute level to calculate the total utility can uncover the customers' preferred attribute level portfolio. The calculation results identified the most preferred tour package. The study found that the greater the variety of package tours to China provided by the web sites of a particular travel agent, the higher the possibility of that agent providing discount incentives. Furthermore, the text content of each web site provides more attribute information regarding package tours and less information about the consequences of travelling and value satisfaction.

This is one of the first studies to provide a methodology integrating conjoint analysis and the means‐end chain approach. Understanding the written content of web sites preferred by Chinese people can help marketers and web site designers design web sites attractive to this market.

The purpose of this paper is to analyze the readability and level of word complexity of search engine results pages (SERPs) snippets and associated web pages between Google and Bing.

The authors employed the Readability Test Tool to analyze the readability and word complexity of 3,000 SERPs snippets and 3,000 associated pages in Google and Bing retrieved on 150 search queries issued by middle school children.

A significant difference was found in the readability of SERPs snippets and associated web pages between Google and Bing. A significant difference was also observed in the number of complex words in snippets between the two engines but not in associated web pages. At the engine level, the readability of Google and Bing snippets was significantly higher than associated web pages. The readability of Google SERPs snippets was at a much higher level than those of Bing. The readability of snippets in both engines mismatched with the reading comprehension of children in grades 6–8.

The data corpus may be small. Analysis relied on quantitative measures.

Practitioners and other mediators should mitigate the readability issue in SERPs snippets. Researchers should consider text readability and word complexity simultaneously with other factors to obtain the nuanced understanding of young users’ web information behaviors. Additional theoretical and methodological implications are discussed.

This study measured the readability and the level of word complexity embedded in SERPs snippets and compared them to respective web pages in Google and Bing. Findings provide further evidence of the readability issue of SERPs snippets and the need to solve this issue through system design improvements.

Search engines exist to help sort through all the information available on the Internet, but have thus fair failed to shoulder any responsibility for the content which appears on the pages they present in their indexes. Search engines lack any transparency to clarify how results were found, and how they are connected to the search terms. Thus, problems arise in connection with the protection of minors – namely, that minors have access, intentional or unwitting, to content which may be harmful to them. The findings of this study point to the need for a better framework for the protection of children. This framework should include codes of conduct for search engines, more accurate labeling of Web site data, and the outlawing of search engine manipulation. This study is intended as a first step in making the public aware of the problem of protecting children on the Internet.

Digital collection assessment has focused mainly on evaluating systems, metadata and usability. While use evaluation is discussed in the literature, there are no standard criteria and methods for how to perform assessment on use effectively. This paper asserts that use statistics have complexities that prohibit meaningful interpretation and assessment. The authors aim to discover the problems inherent in the assessment of digital collection use statistics and propose solutions to address such issues.

This paper identifies and demonstrates five inherent problems with use statistics that need to be addressed when doing assessment for digital collections using the statistics of assessment tools on local digital repositories. The authors then propose solutions to resolve the problems that present themselves upon such analysis.

The authors identified five problems with digital collection use statistics. Problem one is the difficulty of distinguishing different kinds of internet traffic. Problem two is the lack of direct correlation of a digital item to its multiple URLs, so statistics from external web analytics tools are not ideal. Problem three is the analytics tools’ inherent bias in statistics that are counted only in the positive way. Problem four is the different interaction between digital collections with search engine indexing. Problem five is the evaluator’s bias toward simple growing statistics over time for surmising a positive use assessment. Because of these problems, statistics on digital collections do not properly measure a digital library’s value.

Findings highlight problems with current use measures and offer improvements.

This paper identifies five problems that need to be addressed before a meaningful assessment of digital collection use statistics can take place. The paper ends with a call for evaluators to try to solve or mitigate the stated problems for their digital collections in their own evaluations.

This study demonstrates that aggregated data from the Repository Analytics and Metrics Portal (RAMP) have significant potential to analyze visibility and use of institutional repositories (IR) as well as potential factors affecting their use, including repository size, platform, content, device and global location. The RAMP dataset is unique and public.

The webometrics methodology was followed to aggregate and analyze use and performance data from 35 institutional repositories in seven countries that were registered with the RAMP for a five-month period in 2019. The RAMP aggregates Google Search Console (GSC) data to show IR items that surfaced in search results from all Google properties.

The analyses demonstrate large performance variances across IR as well as low overall use. The findings also show that device use affects search behavior, that different content types such as electronic thesis and dissertation (ETD) may affect use and that searches originating in the Global South show much higher use of mobile devices than in the Global North.

The RAMP relies on GSC as its sole data source, resulting in somewhat conservative overall numbers. However, the data are also expected to be as robot free as can be hoped.

This may be the first analysis of aggregate use and performance data derived from a global set of IR, using an openly published dataset. RAMP data offer significant research potential with regard to quantifying and characterizing variances in the discoverability and use of IR content.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-08-2020-0328

To compare the performance of different search engines, highlighting the overlap and rank differences.

Presents results of an overlap test search series among traditional CD‐ROM indexing/abstracting databases since the mid‐1980s, web databases, and authors' own polysearch engine, and reviews Google Scholar.

Finds that overlap is minimal among web‐wide search engines which crawl and index the mostly unstructured open web; and that overlap among Google Scholar and the native search engines is far less than the ideal 100 per cent in the optimal context of crawling and indexing highly‐structured and metadata‐rich collections.

Reinforces the existing view that for comprehensive searches one must search more than one database. Highlights and recommends several very good search engine sites.