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Identification of topic changes within a user search session is a key issue in content analysis of search engine user queries. Recently, various studies have focused on new topic identification/session identification of search engine transaction logs, and several problems regarding the estimation of topic shifts and continuations were observed in these studies. This study aims to analyze the reasons for the problems that were encountered as a result of applying automatic new topic identification.

Measures, such as cleaning the data of common words and analyzing the errors of automatic new topic identification, are applied to eliminate the problems in estimating topic shifts and continuations.

The findings show that the resulting errors of automatic new topic identification have a pattern, and further research is required to improve the performance of automatic new topic identification.

Improving the performance of automatic new topic identification would be valuable to search engine designers, so that they can develop new clustering and query recommendation algorithms, as well as custom‐tailored graphical user interfaces for search engine users.

One of the most important dimensions of search engine user information seeking behaviour is content‐based behaviour. One of the main elements in developing a personalised intelligent search engine is new topic identification. The purpose of this study is to perform automatic new topic identification in search engine transaction logs using conditional probabilities of new topic arrivals.

Sample data logs from FAST (currently owned by Yahoo!) and Excite (currently owned by IAC Search & Media) are used in the study. Conditional probabilities of new topic arrivals and topic continuations given query category are used to estimate new topic arrivals.

The findings of this study show that the conditional probability approach reduced overestimation of topic shifts, increasing some performance measures to their highest ever value compared to previous studies. A straightforward procedure such as the conditional probability approach can be as successful as, and for some measures more successful than, more complex methods applied in previous automatic new topic identification studies.

A straightforward procedure that can enable fast automatic new topic identification, a problem not yet solved, and an important step towards personalised search engines.

Content analysis of search engine user queries is an important task, since successful exploitation of the content of queries can result in the design of efficient information retrieval algorithms of search engines, which can offer custom‐tailored services to the web user. Identification of topic changes within a user search session is a key issue in content analysis of search engine user queries. The purpose of this study is to address these issues.

This study applies genetic algorithms and Dempster‐Shafer theory, proposed by He et al., to automatically identify topic changes in a user session by using statistical characteristics of queries, such as time intervals and query reformulation patterns. A sample data log from the Norwegian search engine FAST (currently owned by overture) is selected to apply Dempster‐Shafer theory and genetic algorithms for identifying topic changes in the data log.

As a result, 97.7 percent of topic shifts and 87.2 percent of topic continuations were estimated correctly. The findings are consistent with the previous application of the Dempster‐Shafer theory and genetic algorithms on a different search engine data log. This finding could be implied as an indication that content‐ignorant topic identification, using query patterns and time intervals, is a promising line of research.

Studies an important dimension of user behavior in information retrieval.

The purpose of this study is to investigate whether question and keyword‐format queries are more successfully processed by search engines encouraging answers to searching and keyword‐format querying, respectively. This study aims to investigate whether web user characteristics and choice of search engine affects the relevancy scores and precision of the results.

The results of two search engines, Google and AskJeeves, were compared for question and keyword‐format queries. It was observed that AskJeeves was slightly more successful in processing question‐format queries, but this finding was not statistically supported. However, Google provided results on keyword‐format queries and the entire set of queries, which were statistically superior to those of AskJeeves.

Analysis of variance (ANOVA) showed that the age of web user is not as affective on the relevancy score and precision of results as other factors. Interactions of the main factors were also affective on the relevancy scores and precision, meaning that the different combinations of various factors cause a synergy in terms of relevancy scores and precision.

This was a preliminary work on the effect of user characteristics on comprehension and evaluation of search query results. Future work includes expanding this study to include more web user characteristics, more levels of the web user characteristics, and inclusion of more search engines.

The findings of this study provide statistical proof for the relationship between the characteristics of web users, choice of search engine and the relevancy scores and precision of search results.

This study aims to propose an artificial neural network to identify automatically topic changes in a user session by using the statistical characteristics of queries, such as time intervals and query reformulation patterns.

A sample data log from the Norwegian search engine FAST (currently owned by Overture) is selected to train the neural network and then the neural network is used to identify topic changes in the data log.

A total of 98.4 percent of topic shifts and 86.6 percent of topic continuations were estimated correctly.

Content analysis of search engine user queries is an important task, since successful exploitation of the content of queries can result in the design of efficient information retrieval algorithms for search engines, which can offer custom‐tailored services to the web user. Identification of topic changes within a user search session is a key issue in the content analysis of search engine user queries.

Recent studies show that many Web users only submit short queries and conduct short search sessions. This paper examines aspects of users’ attempting longer more complex queries. Web search services such as Ask Jeeves – publicly accessible question and answer (Q&A) search engines – encourage queries in question or request format. In light of this trend, this study examines whether general Web queries are shifting towards a more question/request format. Previous studies show that some users were submitting question or request format queries to general non‐Q&A Web search engines. This paper re‐examines this issue by analysing large‐scale Web query data from two different (US and European) Web query data sets, including 1.2 million Excite queries (www.excite.com) and 1.2 million AlltheWeb.com (http://AlltheWeb.com) queries from 2001.

This editorial aims to discuss how technology has transformed the cultural map for libraries so that experiences in Taiwan, Iran, India, Greece, or New Zealand are relevant anywhere in the developed world.

The methodology draws heavily on cultural anthropology in looking at the cultural context and its changes over time.

Four sets of shared problems emerge in these articles: technology applications to solve specific library problems, user studies, cataloging issues, and electronic publishing. These are topics that librarians discuss around the world.

It is a commonplace today that technology has tended to equalize widely separated parts of the world. This is obviously true in terms of consumer goods, but appears to be less well accepted in terms of scholarly endeavor. The international and intellectual breadth of this issue can be seen as something to celebrate.

The purpose of this paper is to resolve a gap in the knowledge of how people with dyslexia interact with information retrieval (IR) systems, specifically an understanding of their information‐searching behaviour.

The dyslexia cognitive profile is used to design a logging system, recording the difference between two sets of participants: dyslexic and control users. A standard Okapi interface is used – together with two standard TREC topics – in order to record the information searching behaviour of these users.

Using the log data, the differences in information‐searching behaviour of control and dyslexic users, i.e. in the way the two groups interact with Okapi, are established and it also established that qualitative information collected (such as experience etc.) may not be able to account for these differences. Evidence from query variables was unable to distinguish between groups, but differences on topic for the same variables were recorded. Users who view more documents tended to judge more documents as being relevant, in terms of either the user group or topic. Session data indicated that there may be an important difference between the number of iterations used in a search between the user groups, as there may be little effect from the topic on this variable.

This is the first study of the effect of dyslexia on information search behaviour, and it provides some evidence to take the field forward.