Webometrics is concerned with measuring aspects of the web: web sites, web pages, parts of web pages, words in web pages, hyperlinks, web search engine results. The importance of the web itself as a communication medium and for hosting an increasingly wide array of documents, from journal articles to holiday brochures, needs no introduction. Given this huge and easily accessible source of information, there are limitless possibilities for measuring or counting on a huge scale (e.g., the number of web sites, the number of web pages, the number of blogs) or on a smaller scale (e.g., the number of web sites in Ireland, the number of web pages in the CNN web site, the number of blogs mentioning Barack Obama before the 2008 presidential campaign). This book argues that it can be useful for social scientists to measure aspects of the web and explains how this can be achieved on both a small and large scale. The book is intended for social scientists with research topics that are wholly or partly online (e.g., social networks, news, political communication) and social scientists with offline research topics with an online reflection, even if this is not a core component (e.g., diaspora communities, consumer culture, linguistic change). The book is also intended for library and information science students in the belief that the knowledge and techniques described will be useful for them to guide and aid other social scientists in their research. In addition, the techniques and issues are all directly relevant to library and information science research problems. Table of Contents: Introduction / Web Impact Assessment / Link Analysis / Blog Searching / Automatic Search Engine Searches: LexiURL Searcher / Web Crawling: SocSciBot / Search Engines and Data Reliability / Tracking User Actions Online / Advaned Techniques / Summary and Future Directions

Webometrics is concerned with measuring aspects of the web: web sites, web pages, parts of web pages, words in web pages, hyperlinks, web search engine results. The importance of the web itself as a communication medium and for hosting an increasingly wide array of documents, from journal articles to holiday brochures, needs no introduction. Given this huge and easily accessible source of information, there are limitless possibilities for measuring or counting on a huge scale (e.g., the number of web sites, the number of web pages, the number of blogs) or on a smaller scale (e.g., the number of web sites in Ireland, the number of web pages in the CNN web site, the number of blogs mentioning Barack Obama before the 2008 presidential campaign). This book argues that it can be useful for social scientists to measure aspects of the web and explains how this can be achieved on both a small and large scale. The book is intended for social scientists with research topics that are wholly or partly online (e.g., social networks, news, political communication) and social scientists with offline research topics with an online reflection, even if this is not a core component (e.g., diaspora communities, consumer culture, linguistic change). The book is also intended for library and information science students in the belief that the knowledge and techniques described will be useful for them to guide and aid other social scientists in their research. In addition, the techniques and issues are all directly relevant to library and information science research problems. Table of Contents: Introduction / Web Impact Assessment / Link Analysis / Blog Searching / Automatic Search Engine Searches: LexiURL Searcher / Web Crawling: SocSciBot / Search Engines and Data Reliability / Tracking User Actions Online / Advaned Techniques / Summary and Future Directions

With the rapid growth of web search in recent years the problem of modeling its users has started to attract more and more attention of the information retrieval community. This has several motivations. By building a model of user behavior we are essentially developing a better understanding of a user, which ultimately helps us to deliver a better search experience. A model of user behavior can also be used as a predictive device for non-observed items such as document relevance, which makes it useful for improving search result ranking. Finally, in many situations experimenting with real users is just infeasible and hence user simulations based on accurate models play an essential role in understanding the implications of algorithmic changes to search engine results or presentation changes to the search engine result page. In this survey we summarize advances in modeling user click behavior on a web search engine result page. We present simple click models as well as more complex models aimed at capturing non-trivial user behavior patterns on modern search engine result pages. We discuss how these models compare to each other, what challenges they have, and what ways there are to address these challenges. We also study the problem of evaluating click models and discuss the main applications of click models.

In recent years there has been an increasing demand for research evaluation within universities and other research-based organisations. In parallel, there has been an increasing recognition that traditional citation-based indicators are not able to reflect the societal impacts of research and are slow to appear. This has led to the creation of new indicators for different types of research impact as well as timelier indicators, mainly derived from the Web. These indicators have been called altmetrics, webometrics or just web metrics. This book describes and evaluates a range of web indicators for aspects of societal or scholarly impact, discusses the theory and practice of using and evaluating web indicators for research assessment and outlines practical strategies for obtaining many web indicators. In addition to describing impact indicators for traditional scholarly outputs, such as journal articles and monographs, it also covers indicators for videos, datasets, software and other non-standard scholarly outputs. The book describes strategies to analyse web indicators for individual publications as well as to compare the impacts of groups of publications. The practical part of the book includes descriptions of how to use the free software Webometric Analyst to gather and analyse web data. This book is written for information science undergraduate and Master’s students that are learning about alternative indicators or scientometrics as well as Ph.D. students and other researchers and practitioners using indicators to help assess research impact or to study scholarly communication.

Today, Web search is treated as a solitary experience. Web browsers and search engines are typically designed to support a single user, working alone. However, collaboration on information-seeking tasks is actually commonplace. Students work together to complete homework assignments, friends seek information about joint entertainment opportunities, family members jointly plan vacation travel, and colleagues jointly conduct research for their projects. As improved networking technologies and the rise of social media simplify the process of remote collaboration, and large, novel display form-factors simplify the process of co-located group work, researchers have begun to explore ways to facilitate collaboration on search tasks. This lecture investigates the who, what, where, when and why of collaborative search, and gives insight in how emerging solutions can address collaborators' needs. Table of Contents: Introduction / Who? / What? / Where? / When? / Why? / Conclusion: How?

The wealth of information accessible on the Internet has grown exponentially since its advent. This mass of content must be systemically sifted to glean pertinent data, and the utilization of the collective intelligence of other users, or social information retrieval, is an innovative, emerging technique. Social Information Retrieval Systems: Emerging Technologies & Applications for Searching the Web Effectively provides relevant content in the areas of information retrieval systems, services, and research; covering topics such as social tagging, collaborative querying, social network analysis, subjective relevance judgments, and collaborative filtering. Answering the increasing demand for authoritative resources on Internet technologies, this Premier Reference Source will make an indispensable addition to any library collection.

This lecture presents an overview of the Web analytics process, with a focus on providing insight and actionable outcomes from collecting and analyzing Internet data. The lecture first provides an overview of Web analytics, providing in essence, a condensed version of the entire lecture. The lecture then outlines the theoretical and methodological foundations of Web analytics in order to make obvious the strengths and shortcomings of Web analytics as an approach. These foundational elements include the psychological basis in behaviorism and methodological underpinning of trace data as an empirical method. These foundational elements are illuminated further through a brief history of Web analytics from the original transaction log studies in the 1960s through the information science investigations of library systems to the focus on Websites, systems, and applications. Following a discussion of on-going interaction data within the clickstream created using log files and page tagging for analytics of Website and search logs, the lecture then presents a Web analytic process to convert these basic data to meaningful key performance indicators in order to measure likely converts that are tailored to the organizational goals or potential opportunities. Supplementary data collection techniques are addressed, including surveys and laboratory studies. The overall goal of this lecture is to provide implementable information and a methodology for understanding Web analytics in order to improve Web systems, increase customer satisfaction, and target revenue through effective analysis of user–Website interactions. Table of Contents: Understanding Web Analytics / The Foundations of Web Analytics: Theory and Methods / The History of Web Analytics / Data Collection for Web Analytics / Web Analytics Fundamentals / Web Analytics Strategy / Web Analytics as Competitive Intelligence / Supplementary Methods for Augmenting Web Analytics / Search Log Analytics / Conclusion / Key Terms / Blogs for Further Reading / References

Question answering (QA) systems on the Web try to provide crisp answers to information needs posed in natural language, replacing the traditional ranked list of documents. QA, posing a multitude of research challenges, has emerged as one of the most actively investigated topics in information retrieval, natural language processing, and the artificial intelligence communities today. The flip side of such diverse and active interest is that publications are highly fragmented across several venues in the above communities, making it very difficult for new entrants to the field to get a good overview of the topic. Through this book, we make an attempt towards mitigating the above problem by providing an overview of the state-of-the-art in question answering. We cover the twin paradigms of curated Web sources used in QA tasks ‒ trusted text collections like Wikipedia, and objective information distilled into large-scale knowledge bases. We discuss distinct methodologies that have been applied to solve the QA problem in both these paradigms, using instantiations of recent systems for illustration. We begin with an overview of the problem setup and evaluation, cover notable sub-topics like open-domain, multi-hop, and conversational QA in depth, and conclude with key insights and emerging topics. We believe that this resource is a valuable contribution towards a unified view on QA, helping graduate students and researchers planning to work on this topic in the near future.

In this book, we aim to provide a fairly comprehensive overview of the scalability and efficiency challenges in large-scale web search engines. More specifically, we cover the issues involved in the design of three separate systems that are commonly available in every web-scale search engine: web crawling, indexing, and query processing systems. We present the performance challenges encountered in these systems and review a wide range of design alternatives employed as solution to these challenges, specifically focusing on algorithmic and architectural optimizations. We discuss the available optimizations at different computational granularities, ranging from a single computer node to a collection of data centers. We provide some hints to both the practitioners and theoreticians involved in the field about the way large-scale web search engines operate and the adopted design choices. Moreover, we survey the efficiency literature, providing pointers to a large number of relatively important research papers. Finally, we discuss some open research problems in the context of search engine efficiency.

Collaboration among scholars has always been recognized as a fundamental feature of scientific discovery. The ever-increasing diversity among disciplines and complexity of research problems makes it even more compelling to collaborate in order to keep up with the fast pace of innovation and advance knowledge. Along with the rapidly developing Internet communication technologies and the increasing popularity of the social web, we have observed many important developments of scholarly collaboration on the academic social web. In this book, we review the rapid transformation of scholarly collaboration on various academic social web platforms and examine how these platforms have facilitated academics throughout their research lifecycle—from forming ideas, collecting data, and authoring articles to disseminating findings. We refer to the term "academic social web platforms" in this book as a category of Web 2.0 tools or online platforms (such as CiteULike, Mendeley, Academia.edu, and ResearchGate) that enable and facilitate scholarly information exchange and participation. We will also examine scholarly collaboration behaviors including sharing academic resources, exchanging opinions, following each other's research, keeping up with current research trends, and, most importantly, building up their professional networks. Inspired by the model developed Olson et al. [2000] on factors for successful scientific collaboration, our examination of the status of scholarly collaboration on the academic social web has four emphases: technology readiness, coupling work, building common ground, and collaboration readiness. Finally, we talk about the insights and challenges of all these online scholarly collaboration activities imposed on the research communities who are engaging in supporting online scholarly collaboration. This book aims to help researchers and practitioners understand the development of scholarly collaboration on the academic social web, and to build up an active community of scholars who are interested in this topic.