Search

Introduction: The latest decade, an emerging issue has been the abuse potential of the gabapentinoids pregabalin and gabapentin. The aim of our study was to assess this safety signal combining two different methods of surveillance: search analytics big data and the FDA spontaneous reporting system database.Methods: Analysis of big data and the FAERS was used to detect pregabalin's and gabapentin's abuse potential in comparison with two controls, clonazepam and levetiracetam, and further, the correlation between these domains was investigated. Data from the United States between 2007 and 2020Q2 were analyzed.Results: The FAERS analysis revealed the following pattern of signals: clonazepam > pregabalin ≥ gabapentin > levetiracetam, for both the primary term “drug abuse and dependence” and the secondary terms (withdrawal, tolerance, overdose). The Google domain pattern was slightly different: clonazepam ≥ gabapentin ≥ pregabalin≥ levetiracetam. A monotonic correlation was found between FAERS and Google searches for gabapentin (r = 0.558; p < 0.001), pregabalin (r = 0.587; p < 0.001), and clonazepam (r = 0.295; p = 0.030).Conclusion: Our results revealed that there is preliminary evidence of a safety signal for the abuse potential of pregabalin and gabapentin. Analysis of the FAERS database, supplemented by big data search analytics, suggests that there is potential of using these methods as a supplementary tool to detect drug abuse-related safety signals in pharmacovigilance.

We describe online consumers’ search behavior for differentiated durable goods using a data set that captures a detailed level of consumer search and attribute information for digital cameras. Consumers search extensively, engaging in 14 searches on average prior to purchase. Individual level search is confined to a small part of the attribute space. Early search is highly predictive of the characteristics of the camera eventually purchased. Search paths through the attribute space are state dependent and display “lock-in” as the search unfolds. Finally, the first-time discovery of the chosen alternative usually takes place toward the end of the search sequence. We discuss these and other findings in the context of optimal search strategies and discuss the prospects for consumer learning during search. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2016.0977 .

Digital marketing is often detrimental if it is done by unskilled service providers.The hyped services of search engine marketing are not as successful as they seem to be.SEM services provided by small organizations and freelancers are generally detrimental.SEM services provided by established players are relatively more beneficial and reliable. The study highlights how digital marketing is often detrimental, when it is done by unskilled service providers. It highlights how the hyped services of search engine marketing (SEM) are not as successful as they seem to be and sometimes affect firms negatively. This study uses social media analytics to derive insights from Twitter using descriptive, content and network analytics. Methods like hashtag analysis, polarity and emotion analysis, word analysis, topic modeling and other relevant approaches have been used to mine user generated content. A qualitative case study on an e-market is used for validation of findings. SEM services provided by small organizations and freelancers are not as beneficial as the ones by established players. The services provided by these firms proved detrimental for the customers based on user experiences surrounding these services in the social media and forum specific discussions. This study highlights how SEM often not only fails to provide benefits but also destructs value if not done properly. Transaction costs like agency problems, coordination costs, loss of non-contractible value and cost of fit are also identified with potential fallouts which affect the long-term benefits. Inputs will be beneficial to practice in planning SEM and outsourcing.

What are the intents or goals behind human interactions with image search engines? Knowing why people search for images is of major concern to Web image search engines because user satisfaction may vary as intent varies. Previous analyses of image search behavior have mostly been query-based, focusing on what images people search for, rather than intent-based, that is, why people search for images. To date, there is no thorough investigation of how different image search intents affect users' search behavior. In this paper, we address the following questions: (1) Why do people search for images in text-based Web image search systems? (2) How does image search behavior change with user intent? (3) Can we predict user intent effectively from interactions during the early stages of a search session? To this end, we conduct both a lab-based user study and a commercial search log analysis. We show that user intents in image search can be grouped into three classes: Explore/Learn, Entertain, and Locate/Acquire. Our lab-based user study reveals different user behavior patterns under these three intents, such as first click time, query reformulation, dwell time and mouse movement on the result page. Based on user interaction features during the early stages of an image search session, that is, before mouse scroll, we develop an intent classifier that is able to achieve promising results for classifying intents into our three intent classes. Given that all features can be obtained online and unobtrusively, the predicted intents can provide guidance for choosing ranking methods immediately after scrolling.

The purpose of this research is how to optimize a web design that can increase the number of visitors. The number of Internet users in the world continues to grow in line with advances in information technology. Products and services marketing media do not just use the printed and electronic media. Moreover, the cost of using the Internet as a medium of marketing is relatively inexpensive when compared to the use of television as a marketing medium. The penetration of the internet as a marketing medium lasted for 24 hours in different parts of the world. But to make an internet site into a site that is visited by many internet users, the site is not only good from the outside view only. Web sites that serve as a medium for marketing must be built with the correct rules, so that the Web site be optimal marketing media. One of the good rules in building the internet site as a marketing medium is how the content of such web sites indexed well in search engines like google. Search engine optimization in the index will be focused on the search engine Google for 83% of internet users across the world using Google as a search engine. Search engine optimization commonly known as SEO (Search Engine Optimization) is an important rule that the internet site is easier to find a user with the desired keywords.

Modern search applications feature real-time as-you-type query search. In its elementary form, the problem consists in retrieving a set of k search results, that is, performing a search with a given prefix, and showing the top-ranked results. In this article, we focus on as-you-type keyword search over social media, that is, data published by users who are interconnected through a social network. We adopt a “network-aware” interpretation for information relevance, by which information produced by users who are closer to the user issuing a request is considered more relevant. This query model raises new challenges for effectiveness and efficiency in online search, even when the intent of the user is fully specified, as a complete query given as input in one keystroke. This is mainly because it requires a joint exploration of the social space and traditional IR indexes, such as inverted lists. We describe a memory-efficient and incremental prefix-based retrieval algorithm, which also exhibits an anytime behavior, allowing output of the most likely answer within any chosen runtime limit. We evaluate our approach through extensive experiments for several applications and search scenarios. We consider searching for posts in microblogging (Twitter and Tumblr), for businesses (Yelp), as well as for movies (Amazon) based on reviews. We also conduct a series of experiments comparing our algorithm with baselines using state-of-the-art techniques and measuring the improvements brought by several key optimizations. They show that our solution is effective in answering real-time as-you-type searches over social media.

The present study addressed the age-related differences in query production and information searching performance when interacting with a search engine. To this end, 20 older adults and 20 young ones performed 12 information search problems of varying complexity in two knowledge domains (health and fantastic movies). Participants had simple (useful keywords provided and answer directly accessible in Google), inferential (inferences to produce useful keywords required) and multicriteria problems (information gathering and navigation required). Results showed that older adults produced their first query with more keywords extracted from the search problem statements and spent more time on the search engine pages than young ones. In the fantastic movies, older adults struggled more than young ones and had difficulties reformulating their queries (i.e. fewer new keywords produced, more statement provided keywords). Older adults especially struggled at the beginning of the search (more time spent on the first search engine result page than young ones and they produced less elaborate initial query). In contrast, in the health domain, higher prior knowledge helped older users reformulate queries that were more elaborated (i.e.no age-related differences on the number of new keywords) and improved the processing of the first search engine page consulted. Role of age and domain knowledge on querying and search engine processing.Impact of search strategies early in the task and overall.Older adults' prior knowledge improved querying and the first stage of the search.

The World Wide Web is a vast and continuously changing source of information where searching is a frequent, and sometimes critical, user task. Searching is not always the user’s primary goal but an ancillary task that is performed to find complementary information allowing to complete another task. In this paper, we explore primary and/or ancillary search tasks and propose an approach for simplifying the user interaction during search tasks. Rather than focusing on dedicated search engines, our approach allows the user to abstract search engines already provided by Web applications into pervasive search services that will be available for performing searches from any other Web site. We also propose allowing users to manage the way in which the search results are presented and some possible interactions. In order to illustrate the feasibility of this approach, we have built a support tool based on a plug-in architecture that allows users to integrate new search services (created by themselves by means of visual tools) and execute them in the context of both kinds of searches. A case study illustrates the use of such tool. We also present the results of two evaluations that demonstrate the feasibility of the approach and the benefits in its use.

Providing support for users during their search sessions has\ud been hailed as a major challenge in interactive information retrieval\ud (IIR). Providing such support requires considering the context of the\ud search and facilitating the work task at hand. In this paper, we consider\ud the work tasks associated with air traffic analysts, who perform numerous\ud searches using a multifaceted search interface in order to acquire business\ud intelligence regarding particular events and situations. In particular, we\ud develop a novel task completion engine and seamlessly incorporated it\ud within a current air traffic search system to facilitate the comparison\ud of information objects found. In a study with 24 participants, we found\ud that they completed the complex work task faster using the comparison\ud feature, but for simple work tasks, participants were slower. However,\ud participants reported (statistically) significantly higher satisfaction and\ud had (statistically) significantly higher accuracy using the search system\ud equipped with task completion engine. These findings help to steer systems to provide a better support to users in their search process.

A major challenge of the contemporary information age is the overwhelming and increasing data amount, especially when looking for specific information. Searching for relevant information is no longer manually possible, but has to rely on automatic methods, specifically, similarity search. From a formal perspective, similarity search can be seen as the problem of finding entities, which are considered to be similar to a query with respect to certain describing features. The question which features or which weighted combination of features to use for a given query creates a need for semi-automatic methods to address the needs of diverse users. Furthermore, the quality of the results of a similarity search is more than effectiveness, measured by precision and recall. The user ideally needs to trust the results and understand how they were computed. We propose to apply Visual Analytics methodologies, for synergistic cooperation of user and algorithms, to integrate three key dimensions of similarity search: users, tasks, and data for effective search. However, there exists a gap in knowledge how user, task as well as the available data influence each other and the similarity search. In this concept paper, we envision how Visual Analytics can be used to tackle current challenges of similarity search.

In this paper, we present SaR-Web, a multimodal web search tool that provides automatic support to searching as learning processes. Inspired by the work of Richard Rogers and the Digital Methods Initiative, SaR-Web compares the results of queries across search engine language domains, and visualizes search results with a semantic added value, thus facilitating cross-linguistic and cross-cultural comparisons of results. The comparison between search results in different languages is enabled through the visualization of semantic concepts extracted by means of a NER tool from the search results. The SaR-Web system has the potential to support highlevel learning activities described in Bloom's taxonomy such as: identifying and analyzing patterns, comparing, integrating, and creating new ideas.

The availability of web search engines offers opportunities in addition to those provided by bibliographic databases for identifying academic literature, but their usefulness for retrieving research is uncertain. A rigorous literature search was undertaken to investigate whether web search engines might replace bibliographic databases, using empirical research in health and social care as a case study. Eight databases and five web search engines were searched between 20 July and 6 August 2015. Sixteen unique studies which compared at least one database with at least one web search engine were examined, as well as drawing lessons from the authors’ own search process. Web search engines were limited in that the searcher cannot be certain that the principles of Boolean logic apply and they were more limited than bibliographic databases in their functions, such as exporting abstracts. Recommendations are made for improving the rigour and quality of reporting studies of academic literature searching.

Every company has a huge base for managing their knowledge and know-how. As this base expands with time, the process of searching becomes more complex. This is seen especially with the traditional search engines as they offer a vast amount of results which are generally irrelevant to users. In this paper, we present our multi-agent approach to personalize the search results by user profile. The proposed approach ranks the search results of each user by their centers of interests or search history.

Background: In spite of the enormous amount of information available on the Web and the fact that search engines are continuously evolving to enhance the search experience, students are nevertheless faced with the difficulty of effectively retrieving information. It is, therefore, imperative for the interaction between students and search tools to be understood and search strategies to be identified, in order to promote successful information retrieval. Objectives: This study identifies the Web search strategies used by postgraduate students and forms part of a wider study into information retrieval strategies used by postgraduate students at the University of KwaZulu-Natal (UKZN), Pietermaritzburg campus, South Africa. Method: Largely underpinned by Thatcher’s cognitive search strategies, the mixed-methods approach was utilised for this study, in which questionnaires were employed in Phase 1 and structured interviews in Phase 2. This article reports and reflects on the findings of Phase 2, which focus on identifying the Web search strategies employed by postgraduate students. The Phase 1 results were reported in Civilcharran, Hughes and Maharaj (2015). Results: Findings reveal the Web search strategies used for academic information retrieval. In spite of easy access to the invisible Web and the advent of meta-search engines, the use of Web search engines still remains the preferred search tool. The UKZN online library databases and especially the UKZN online library, Online Public Access Catalogue system, are being underutilised. Conclusion: Being ranked in the top three percent of the world’s universities, UKZN is investing in search tools that are not being used to their full potential. This evidence suggests an urgent need for students to be trained in Web searching and to have a greater exposure to a variety of search tools. This article is intended to further contribute to the design of undergraduate training programmes in order to deal with the information retrieval frustrations of novice users.

A Spatial Data Infrastructure (SDI) is a framework of geospatial data, metadata, users and tools intended to provide an efficient and flexible way to use spatial information. One of the key software components of an SDI is the catalogue service which is needed to discover, query, and manage the metadata. Catalogue services in an SDI are typically based on the Open Geospatial Consortium (OGC) Catalogue Service for the Web (CSW) standard which defines common interfaces for accessing the metadata information. A search engine is a software system capable of supporting fast and reliable search, which may use “any means necessary” to get users to the resources they need quickly and efficiently. These techniques may include features such as full text search, natural language processing, weighted results, fuzzy tolerance results, faceting, hit highlighting, recommendations, feedback mechanisms based on log mining, usage statistic gathering, and many others. In this paper we will be focusing on improving geospatial search with a search engine platform that uses Lucene, a Java-based search library, at its core. In work funded by the National Endowment for the Humanities, the Centre for Geographic Analysis (CGA) at Harvard University is in the process of re-engineering the search component of its public domain SDI (WorldMap http://worldmap.harvard.edu ) which is based on the GeoNode platform. In the process the CGA has developed Harvard Hypermap (HHypermap), a map services registry and search platform independent from WorldMap. The goal of HHypermap is to provide a framework for building and maintaining a comprehensive registry of web map services, and because such a registry is expected to be large, the system supports the development of clients with modern search capabilities such as spatial and temporal faceting and instant previews via an open API. Behind the scenes HHypermap scalably harvests OGC and Esri service metadata from distributed servers, organizes that information, and pushes it to a search engine. The system monitors services for reliability and uses that to improve search. End users will be able to search the SDI metadata using standard interfaces provided by the internal CSW catalogue, and will benefit from the enhanced search possibilities provided by an advanced search engine. HHypermap is built on an open source software source stack.

Internet has become a platform which reached millions of users momentarily with increased use, also become a place where people spent most of their time during the day by gaining consumer and potential customer ID in addition to just being ordinary Internet users. Search engines also have earned the distinction of being the preferred reference for users in the Internet sea which draws attention with usage rate and allowing you to easily reach the sought-after content where millions of content is added. Search engines has led to the birth of "Search Engine Marketing" concept which is gaining importance by considering the users as potential customers by companies who start to spend most of their time in different platform on the Internet or use search engines to find a subject we want to know, a music we like or any video or image. In the way for businesses to reach the consumers, search engines bring a different awareness in terms of competitiveness to marketing on the internet and are considered as an effective method that is preferred in today's e-marketing activities.Although search engines for Internet users are frequently used as preferred web pages to access any information, at the same time it helps users to reach products or services over the internet with consumer identity. That is why as search engines for businesses and brands can be used in order to increase the possibility of awareness, listed as top rankings, or in a way partitioned according to the criteria specified on the web to attract the attention of consumers, the search engine can also become an effective marketing tool by using completely natural methods without paying any fees to ad units.Especially in recent years search engine optimization which gained great importance and search engine marketing concepts have become concepts that should be examined in the framework of the marketing. That is why in this study the concept search engine optimization which is often preferred in e-marketing strategies was examined in detail in terms of businesses and brands, accordingly search engine marketing concept examined in theory in advertising and communication aspects and discussed in detail in terms of marketing factors.Keywords: Search Engine Marketing, Search Engine Marketing, SEO, SEM, Search Engine Optimization, E-Marketing

OPAC interfaces, still the dominant access point to library catalogs, support systematic search but are problematic for open-ended exploration and generally unpopular with visitors. As a result, libraries start subscribing to simplified search paradigms as exemplified by web-search systems. This is a problem considering that systematic search is a crucial skill in the light of today’s abundance of digital information. Inspired by novel approaches to facilitating search, we designed CollectionDiver, an installation for supporting systematic search in public libraries. The CollectionDiver combines tangible and large display direct-touch interaction with a visual representation of search criteria and filters. We conducted an in-situ qualitative study to compare participants’ search approaches on the CollectionDiver with those on the OPAC interface. Our findings show that while both systems support a similar search process, the CollectionDiver (1) makes systematic search more accessible, (2) motivates proactive search approaches by (3) adding transparency to the search process, and (4) facilitates shared search experiences. We discuss the CollectionDiver’s design concepts to stimulate new ideas toward supporting engaging approaches to systematic search in the library context and beyond. Postprint

This paper provides a method to detect the real location of agency website based on search engine. We will analyze and process the target agency website to obtain the server routing information, extract critical feature information from web content, combine with search engine to acquire web data, and calculate word frequency. Through named entity recognition, web text matching calculation and syntactic analysis, we can infer real location of the target agency website in the real world. The experimental results show that our approach is reliable, correct and effective.

Search engines provide a window into the changing association between websites and keywords across cultures and countries and over time. As such, they offer journalism and news researchers an opportunity to study how search engines, in this case Google, mediate news events and stories online. However, search results are not straightforward to study. Since search results are made in the act of searching and will have to be retrieved from Google Search in real-time, there is a range of different ontological and methodological issues related to this data source. This paper addresses these issues by discussing how factors in the search algorithm can be used proactively to study variations across searchers and in time. The paper identifies various endogenous and exogenous factors in the search algorithm one has to pay attention to and discusses ways to archive search results accordingly. Through a small case study, ways to work with the influence of endogenous factors (keywords, language settings, geo-location, Web history and clicking behaviour) and mitigate the effects of the exogenous factors (experimentation and randomisation) are suggested. Then, a new approach to studying search results is put forward, which builds on purposeful sampling of real-world participantsor constructed research profiles. Finally, perspectives for news and journalism scholars in studying algorithmically generated content in a broader context are offered. Search engines provide a window into the changing association between websites and keywords across cultures and countries and over time. As such, they offer journalism and news researchers an opportunity to study how search engines, in this case Google, mediate news events and stories online. However, search results are not straightforward to study. Since search results are made in the act of searching and will have to be retrieved from Google Search in real-time, there is a range of different ontological and methodological issues related to this data source. This paper addresses these issues by discussing how factors in the search algorithm can be used proactively to study variations across searchers and in time. The paper identifies various endogenous and exogenous factors in the search algorithm one has to pay attention to and discusses ways to archive search results accordingly. Through a small case study, ways to work with the influence of endogenous factors (keywords, language settings, geo-location, Web history and clicking behaviour) and mitigate the effects of the exogenous factors (experimentation and randomisation) are suggested. Then, a new approach to studying search results is put forward, which builds on purposeful sampling of real-world participants or constructed research profiles. Finally, perspectives for news and journalism scholars in studying algorithmically generated content in a broader context are offered.

Innovative contribution to aggregated search.Innovative contribution to the definition of interfaces for visualizing search results.Graph-based visualization of multimedia search results.Multimodal access to media objects.User based evaluation. Nowadays, together with the increasing spread of the content available online, users' information needs have become more complex. To fulfill them, users strongly rely on Web search engines, but traditional ways of presenting search results are often unsatisfactory. In fact, Web pages carry information that exists in multiple media formats, such as text, audio, image and video objects. Vertical search engines and medium-specific search services do not provide users with an integrated view of search results. Furthermore, multiple media objects are in most of the cases highly semantically interlinked, but the connections between them are not sufficiently exploited to provide a further exploration of the retrieved objects. To address these issues, in this paper we propose a graph-based approach aimed at providing users with the possibility to dynamically visualize and explore a search result space built over a repository of multimedia documents containing interconnected multiple media objects. To do this, we represent the search result space via a graph-based data model, where both the retrieved multimedia documents and connected relevant media objects are considered. Media objects, among them, are connected via different kinds of similarity relationships, which depend on the low-level features and metadata taken into consideration to access the media objects. The approach and the connected visualization and exploration interface have been implemented and tested on a publicly available dataset, and they have been evaluated by means of a usability test.

Search engines are the most utilized tools to access information on the Web. The success of large companies such as Google owes to their capacity to conduct users through the vast troves of knowledge and information online. Recently, the concept of search as research has been used to shift the research focus from workings of information-seeking tools towards methods for the social study of Web and particularly the social meanings of engine results. In this paper, we present SaR-Web, a web search tool that provides an automatic means to carry out search as research on the Web. It compares the results of same (translated) queries across search engine language domains, thereby enabling cross-linguistic and cross-cultural comparisons of results. SaR-Web outputs enable the comparative study of cultural mores as well as societal associations and concerns, interpreted through search engine results.

Most information retrieval (IR) systems consider relevance, usefulness, and quality of information objects (documents, queries) for evaluation, prediction, and recommendation, often ignoring the underlying search process of information seeking. This may leave out opportunities for making recommendations that analyze the search process and/or recommend alternative search process instead of objects. To overcome this limitation, we investigated whether by analyzing a searcher���s current processes we could forecast his likelihood of achieving a certain level of success with respect to search performance in the future. We propose a machine-learning-based method to dynamically evaluate and predict search performance several time-steps ahead at each given time point of the search process during an exploratory search task. Our prediction method uses a collection of features extracted from expression of information need and coverage of information. For testing, we used log data collected from 4 user studies that included 216 users (96 individuals and 60 pairs). Our results show 80���90% accuracy in prediction depending on the number of time-steps ahead. In effect, the work reported here provides a framework for evaluating search processes during exploratory search tasks and predicting search performance. Importantly, the proposed approach is based on user processes and is independent of any IR system.

The Internet provides a quick access to a plethora of the medical literature, in the form of journals, databases, dictionaries, textbooks, indexes, and e-journals, thereby allowing access to more varied, individualized, and systematic educational opportunities. Web search engine is a tool designed to search for information on the World Wide Web, which may be in the form of web pages, images, information, and other types of files. Search engines for internet-based search of medical literature include Google, Google scholar, Yahoo search engine, etc., and databases include MEDLINE, PubMed, MEDLARS, etc. Commercial web resources (Medscape, MedConnect, MedicineNet) add to the list of resource databases providing some of their content for open access. Several web-libraries (Medical matrix, Emory libraries) have been developed as meta-sites, providing useful links to health resources globally. Availability of specific dermatology-related websites (DermIs, DermNet, and Genamics Jornalseek) is useful addition to the ever growing list of web-based resources. A researcher must keep in mind the strengths and limitations of a particular search engine/database while searching for a particular type of data. Knowledge about types of literature and levels of detail available, user interface, ease of access, reputable content, and period of time covered allow their optimal use and maximal utility in the field of medicine.

CWM Global Search is a meta-search engine allowing chemists and biologists to search the major chemical and biological databases on the Internet, by structure, synonyms, CAS Registry Numbers and free text. A meta-search engine is a search tool that sends user requests to several other search engines and/or databases and aggregates the results into a single list or displays them according to their source [1]. CWM Global Search is a web application that has many of the characteristics of desktop applications (also known as Rich Internet Application, RIA), and it runs on both Windows and Macintosh platforms. The application is one of the first RIA for scientists. The application can be started using the URL http://cwmglobalsearch.com/gsweb.

A fundamental issue in Web search is ranking search results based on user logs, since different users may have different preferences and intents with regards to a search query. Also, in many search query applications, users tend to look at only the top part of the ranked result list in order to find relevant documents. The setting we consider contains various types of users, each of which is interested in a subset of the search results. The goal is to rank the search results of a query providing highly ranked relevant results. Our performance measure is the discounted cumulative gain which offers a graded relevance scale of documents in a search engine result set, and measures the usefulness (gain) of a document based on its position in the result list. Based on this measure, we suggest a general approach to developing approximation algorithms for ranking search results that captures different aspects of users’ intents. We also take into account that the relevance of one document cannot be treated independently of the relevance of other documents in a collection returned by a search engine. We first consider the scenario where users are interested in only a single search result (e.g., navigational queries). We then develop a polynomial time approximation scheme for this case. We further consider the general case where users have different requirements on the number of search results, and develop efficient approximation algorithms. Finally, we consider the problem of choosing the top k out of n search results and show that for this problem (1¿-¿1/e) is indeed the best approximation factor achievable, thus separating the approximability of the two versions of the problem.

• This article argues that search engines in general, and Google Scholar in particular, have become significant co-producers of academic knowledge. Knowledge is not simply conveyed to users, but is co-produced by search engines’ ranking systems and profiling systems, none of which are open to the rules of transparency, relevance and privacy in a manner known from library scholarship in the public domain. Inexperienced users tend to trust proprietary engines as neutral mediators of knowledge and are commonly ignorant of how meta-data enable engine operators to interpret collective profiles of groups of searchers. Theorizing search engines as nodal points in networks of distributed power, based on the notions of Manuel Castells, this article urges for an enriched form of information literacy to include a basic understanding of the economic, political and socio-cultural dimensions of search engines. Without a basic understanding of network architecture, the dynamics of network connections and their intersections, it is hard to grasp the social, legal, cultural and economic implications of search engines. •

The multiple viewed search engine presented here retrieves documents of an indicated search area and displays a matrix of the distribution of the clustering from two aspects of the retrieval result. The search engine provides a visual and semantic bird's-eye view of the entire retrieval result. In addition, the characteristic words of each cluster are displayed in the matrix, and supports narrowing of the search. Furthermore, it is possible to immediately change the analysis criteria or the number of clusters and to use a zooming function. Thus, various retrieval conditions for a query can be attempted immediately and continuously. As a case study, this paper performs several analyses on the electronic journal Zoological Science using a multiple viewed search engine.

We carried out a retrieval effectiveness test on the three major web search engines (i.e., Google, Microsoft and Yahoo). In addition to relevance judgments, we classified the results according to their commercial intent and whether or not they carried any advertising. We found that all search engines provide a large number of results with a commercial intent. Google provides significantly more commercial results than the other search engines do. However, the commercial intent of a result did not influence jurors in their relevance judgments., Measurement, Performance, Experimentation, Worldwide Web, search engines, commerciality, evaluation

Purpose The purpose of this paper is to investigate the search behavior of institutional repository (IR) users in regard to subjects as a means of estimating the potential impact of applying a controlled subject vocabulary to an IR. Design/methodology/approach Google Analytics data were used to record cases where users arrived at an IR item page from an external web search and subsequently downloaded content. Search queries were compared against the Faceted Application of Subject Terminology (FAST) schema to determine the topical nature of the queries. Queries were also compared against the item’s metadata values for title and subject using approximate string matching to determine the alignment of the queries with current metadata values. Findings A substantial portion of successful user search queries to an IR appear to be topical in nature. User search queries matched values from FAST at a higher rate than existing subject metadata. Increased attention to subject description in IR records may provide an opportunity to improve the search visibility of the content. Research limitations/implications The study is limited to a particular IR. Data from Google Analytics does not provide comprehensive search query data. Originality/value The study presents a novel method for analyzing user search behavior to assist IR managers in determining whether to invest in applying controlled subject vocabularies to IR content.

“White hat” search engine optimization refers to the practice of publishing web pages that are useful to humans, while enabling search engines and web applications to better understand the structure and content of your website. This article teaches you to add structured data to your website so that search engines can more easily connect patrons to your library locations, hours, and contact information. A web page for a branch of the Greater Sudbury Public Library retrieved in January 2015 is used as the basis for examples that progressively enhance the page with structured data. Finally, some of the advantages structured data enables beyond search engine optimization are explored

Recent research has shown that mining and modelling search tasks helps improve the performance of search personalisation. Some approaches have been proposed to model a search task using topics discussed in relevant documents, where the topics are usually obtained from human-generated online ontology such as Open Directory Project. A limitation of these approaches is that many documents may not contain the topics covered in the ontology. Moreover, the previous studies largely ignored the dynamic nature of the search task; with the change of time, the search intent and user interests may also change. This paper addresses these problems by modelling search tasks with time-awareness using latent topics, which are automatically extracted from the task's relevance documents by an unsupervised topic modelling method (i.e., Latent Dirichlet Allocation). In the experiments, we utilise the time-aware search task to re-rank result list returned by a commercial search engine and demonstrate a significant improvement in the ranking quality.

Web searchers sometimes struggle to find relevant information. Struggling leads to frustrating and dissatisfying search experiences, even if searchers ultimately meet their search objectives. Better understanding of search tasks where people struggle is important in improving search systems. We address this important issue using a mixed methods study using large-scale logs, crowd-sourced labeling, and predictive modeling. We analyze anonymized search logs from the Microsoft Bing Web search engine to characterize aspects of struggling searches and better explain the relationship between struggling and search success. To broaden our understanding of the struggling process beyond the behavioral signals in log data, we develop and utilize a crowd-sourced labeling methodology. We collect third-party judgments about why searchers appear to struggle and, if appropriate, where in the search task it became clear to the judges that searches would succeed (i.e., the pivotal query). We use our findings to propose ways in which systems can help searchers reduce struggling. Key components of such support are algorithms that accurately predict the nature of future actions and their anticipated impact on search outcomes. Our findings have implications for the design of search systems that help searchers struggle less and succeed more.

Location search engines are an important part of GPS-enabled devices such as mobile phones and tablet computers. In this paper, we study how users behave when they interact with a location search engine by analyzing logs from a popular GPS-navigation service to find out whether mobile users’ location search characteristics differ from those of regular web search. In particular, we analyze query- and session-based characteristics and the temporal distribution of location searches performed on smart phones and tablet computers. Our findings may be used to improve the design of search interfaces in order to help users perform location search more effectively and improve the overall experience on GPS-enabled mobile devices.

Personalization promises to improve the accuracy of Web search and has been drawing much research attention recently. Some evidence indicates that for educational purposes, the disadvantages of personalized search are not justified by its benefits. The potential issues with search personalization, especially in an educational context, include loss of serendipity and capability, commercialization of education and the "Filter Bubble" effect where users are denied information if search engine algorithms decide it is irrelevant to them. The majority of students in higher education make use of general-purpose search engines to find academic information, however we have little knowledge about the effects of personalization on learners' experience and achievements. This observation motivates the research in this paper. First, we surveyed 120 university students to investigate which research sources, including search engines they predominately use and how much they depend on each for educational purposes. We learned that the majority of students prefer Google to other search engines; indeed sometimes it is their primary or only information-seeking tool. Additionally, about 80% of them use search engines for educational purposes on daily basis. Second, we measured the difference between personalized and non-personalized search results for 120 academic search queries divided equally into four categories: Education, IT, Health sciences and Business. Our results showed that on average only 53% of links appear, not necessarily in the same order, in both personalized and non-personalized search results. Interestingly, we observed only slight differences in the extent of personalization based on academic topics. Refereed/Peer-reviewed

Exploratory search confront users with challenges in expressing search intents as the current search interfaces require investigating result listings to identify search directions, iterative typing, and reformulating queries. We present the design of Exploration Wall, a touch-based search user interface that allows incremental exploration and sense-making of large information spaces by combining entity search, flexible use of result entities as query parameters, and spatial configuration of search streams that are visualized for interaction. Entities can be flexibly reused to modify and create new search streams, and manipulated to inspect their relationships with other entities. Data comprising of task-based experiments comparing Exploration Wall with conventional search user interface indicate that Exploration Wall achieves significantly improved recall for exploratory search tasks while preserving precision. Subjective feedback supports our design choices and indicates improved user satisfaction and engagement. Our findings can help to design user interfaces that can effectively support exploratory search on touch devices.

We investigate user requirements regarding the interface design for a semantic multimedia search and retrieval based on a prototypical implementation of a search engine for multimedia content on the web. Thus, unlike existing image search engines and video search engines, we are interested in true multimedia content combining different media assets into multimedia documents like PowerPoint presentations and Flash files. In a user study with 20 participants, we conducted a formative evaluation based on the think-aloud method and semi-structured interviews in order to obtain requirements to a future web search engine for multimedia content. The interviews are complemented by a paper-and-pencil questionnaire to obtain quantitative information and present mockups demonstrating the user interface of a future multimedia search and retrieval engine.