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• Introduction
• Technological Innovations in Response to COVID-19: Research Agenda Considering Marginalized Populations
• Adoption of Artificial Intelligence Technologies by Often Marginalized Populations
• Mining the Health Information Needs of COVID-19 Patients based on Social Q&A Community
• Digital Divide Faced by the Elderly Under the COVID-19 Pandemic
• Difficulties of Vulnerable Groups in Accessing the Public Information Service during COVID-19 Pandemic in China
• Responding to COVID-19: Privacy Implications of the Rapid Adoption of ICTs
• Conclusion. .

This book provides an overview of the impact of the pandemic in China and the USA and presents a research agenda for use, access, and adoption of information and communication technologies (ICT) in the era of COVID-19. The global COVID-19 pandemic changed lives overnight and exposed socially vulnerable populations to ever-challenging situations. One significant challenge was the use, access, and adoption of technological resources. To understand how socially vulnerable populations managed the COVID-19 pandemic and adapt to the new normal, it is important for researchers and practitioners to identify the challenges and understand the perceptions of technologies. Through various research studies, this edited volume addresses the impact of the global COVID-19 pandemic and the adoption of technologies (from artificial intelligence to telehealth and telecommunications) among some socially vulnerable populations (including children, older adults, COVID-19 patients, and general marginalized populations) in the world. The information divide faced by socially vulnerable groups is studied as well as the dimension of vulnerabilities and the impacts of specific technologies.

• Introduction
• Research Genealogy
• Research Genealogy Knowledge Base
• Genealogical History of the Modern World
• Trustworthy Communications
• Summary.

This book discusses how a genealogical history of the modern world can be created by linking the Royal Families of Western Europe to a Unifying Ancestry. This new edition extends the original analysis by including a coherence metric to evaluate the best Unifying Ancestry. The author discusses why common ancestors of the Royal Families of Western Europe comprise an optimal Unifying Ancestry and further illustrates this by using historically influential people as examples. Specifically, algorithms for validating the Unifying Ancestry are applied to a 330,000-person Research Genealogy and then used to link historically influential people to the Unifying Ancestry. Genealogical evaluation properties for consistency, correctness, closure, connectivity, completeness and coherence are demonstrated. These properties are applied to the Research Genealogy to generate a unifying ancestry for western Europeans. The unifying ancestry is then used to create a genealogical history of the modern world. All the analyses can be reproduced by readers using CoreGen3, a freely available online educational software genealogy workbench that is bundled with the Research Genealogy database. Provides algorithms to evaluate properties of genealogies for a variety of factors Utilizes the Unifying Ancestry CoreGen3 workbench analysis program enabling readers to reproduce the presented examples Presents methods to build a complete genealogy and identifies familial relationships between historical figures.

• At the Intersection of Provenance and Metadata.- Introduction to PROV.- PROV Advanced Topics.- ProvONE.- Introduction to PREMIS.- PREMIS Advanced Topics.
• (source: Nielsen Book Data)

This book explores provenance, the study and documentation of how things come to be. Traditionally defined as the origins, source, or ownership of an artifact, provenance today is not limited to historical domains. It can be used to describe what did happen (retrospective provenance), what could happen (subjunctive provenance), or what will happen (prospective provenance). Provenance information is ubiquitous and abundant; for example, a wine label that details the winery, type of grape, and country of origin tells a provenance story that determines the value of the bottle. This book presents select standards used in organizing provenance information and provides concrete examples on how to implement them. Provenance transcends disciplines, and this book is intended for anyone who is interested in documenting workflows and recipes. The goal is to empower readers to frame and answer provenance questions for their own work. Provenance is increasingly important in computational workflows and e-sciences and addresses the need for a practical introduction to provenance documentation with simple-to-use multi-disciplinary examples and activities. Case studies and examples address the creation of basic records using a variety of provenance metadata models, and the differences between PROV, ProvONE, and PREMIS are discussed. Readers will gain an understanding of the uses of provenance metadata in different domains and sectors in order to make informed decisions on their use. Documenting provenance can be a daunting challenge, and with clear examples and explanations, the task will be less intimidating to explore provenance needs.
(source: Nielsen Book Data)

• Introduction.- Correlation-oriented Graph Learning for OCM.- Modality-oriented Graph Learning for OCM.- Unsupervised Disentangled Graph Learning for OCM.- Supervised Disentangled Graph Learning for OCM.- Heterogeneous Graph Learning for Personalized OCM.- Research Frontiers.
• (source: Nielsen Book Data)

This book sheds light on state-of-the-art theories for more challenging outfit compatibility modeling scenarios. In particular, this book presents several cutting-edge graph learning techniques that can be used for outfit compatibility modeling. Due to its remarkable economic value, fashion compatibility modeling has gained increasing research attention in recent years. Although great efforts have been dedicated to this research area, previous studies mainly focused on fashion compatibility modeling for outfits that only involved two items and overlooked the fact that each outfit may be composed of a variable number of items. This book develops a series of graph-learning based outfit compatibility modeling schemes, all of which have been proven to be effective over several public real-world datasets. This systematic approach benefits readers by introducing the techniques for compatibility modeling of outfits that involve a variable number of composing items. To deal with the challenging task of outfit compatibility modeling, this book provides comprehensive solutions, including correlation-oriented graph learning, modality-oriented graph learning, unsupervised disentangled graph learning, partially supervised disentangled graph learning, and metapath-guided heterogeneous graph learning. Moreover, this book sheds light on research frontiers that can inspire future research directions for scientists and researchers. .
(source: Nielsen Book Data)

• Introduction.- Data discovery: A human-centered view.- Data needs.- Discovering data.- Data evaluation and sensemaking.- Recommendations for data discovery, sensemaking and reuse.
• (source: Nielsen Book Data)

This book synthesizes existing research on human-centered data discovery, as well as the recommendations which exist for supporting the design of sustainable, user-centered data search systems. While information-seeking in various settings has been well-researched within computer and information science, not much is known about human-centered data discovery, or how people discover, understand and interact with data that others create. This is particularly relevant given the ever-increasing amounts of data being produced and made available, and the creation of data-specific discovery tools and systems. This book examines how people find the data they need, which search strategies and tools they use, how they understand data, and how search systems can be better designed to meet people's needs.
(source: Nielsen Book Data)

• Proximity and Clues.- More than Meets the Eye.- Epidata, Clues, Threads, and Webs.- Provocations and Invitations.
• (source: Nielsen Book Data)

This book provides a new model to explore discoverability and enhance the meaning of information. The authors have coined the term epidata, which includes items and circumstances that impact the expression of the data in a document, but are not part of the ordinary process of retrieval systems. Epidata affords pathways and points to details that cast light on proximities that might otherwise go unknown. In addition, epidata are clues to mis-and dis-information discernment. There are many ways to find needed information; however, finding the most useable information is not an easy task. The book explores the uses of proximity and the concept of epidata that increases the probability of finding functional information. The authors sketch a constellation of proximities, present examples of attempts to accomplish proximity, and provoke a discussion of the role of proximity in the field. In addition, the authors suggest that proximity is a thread between retrieval constructs based on known topics, predictable relations, and types of information seeking that lie outside constructs such as browsing, stumbling, encountering, detective work, art making, and translation.
(source: Nielsen Book Data)

• Acknowledgments Introduction Data Collection with Mozdeh Word Association Detection: Term Identification Word Association Contextualization: Term Meaning and Context Word Association Thematic Analysis: Theme Detection Word Association Thematic Analysis Examples Comparison Between WATA and Other Methods Ethics Project Planning Summary References Author Biography.
• (source: Nielsen Book Data)

This book explains the word association thematic analysis method, with examples, and gives practical advice for using it. It is primarily intended for social media researchers and students, although the method is applicable to any collection of short texts. Many research projects involve analyzing sets of texts from the social web or elsewhere to get insights into issues, opinions, interests, news discussions, or communication styles. For example, many studies have investigated reactions to Covid-19 social distancing restrictions, conspiracy theories, and anti-vaccine sentiment on social media. This book describes word association thematic analysis, a mixed methods strategy to identify themes within a collection of social web or other texts. It identifies these themes in the differences between subsets of the texts, including female vs. male vs. nonbinary, older vs. newer, country A vs. country B, positive vs. negative sentiment, high scoring vs. low scoring, or subtopic A vs. subtopic B. It can also be used to identify the differences between a topic-focused collection of texts and a reference collection. The method starts by automatically finding words that are statistically significantly more common in one subset than another, then identifies the context of these words and groups them into themes. It is supported by the free Windows-based software Mozdeh for data collection or importing and for the quantitative analysis stages.
(source: Nielsen Book Data)

• Preface Acknowledgments Introduction Setup Getting Started with Simple Questions Complex Question Answering Conversational Question Answering Part I: Summary and Insights Setup Reading Comprehension Open-Domain Question Answering Multi-Hop Question Answering Conversational Question Answering Part II: Summary and Insights Open Directions References Authors' Biographies.
• (source: Nielsen Book Data)

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.
(source: Nielsen Book Data)

• Preface Introduction The Author Name Disambiguation Task
• Foundations Taxonomy Heuristic-Based Hierarchical Clustering Disambiguation SAND: Self-Training Author Name Disambiguator Incremental Author Name Disambiguation Additional Methods for Author Name Disambiguation Bibliography Authors' Biographies.
• (source: Nielsen Book Data)

This book deals with a hard problem that is inherent to human language: ambiguity. In particular, we focus on author name ambiguity, a type of ambiguity that exists in digital bibliographic repositories, which occurs when an author publishes works under distinct names or distinct authors publish works under similar names. This problem may be caused by a number of reasons, including the lack of standards and common practices, and the decentralized generation of bibliographic content. As a consequence, the quality of the main services of digital bibliographic repositories such as search, browsing, and recommendation may be severely affected by author name ambiguity. The focal point of the book is on automatic methods, since manual solutions do not scale to the size of the current repositories or the speed in which they are updated. Accordingly, we provide an ample view on the problem of automatic disambiguation of author names, summarizing the results of more than a decade of research on this topic conducted by our group, which were reported in more than a dozen publications that received over 900 citations so far, according to Google Scholar. We start by discussing its motivational issues (Chapter 1). Next, we formally define the author name disambiguation task (Chapter 2) and use this formalization to provide a brief, taxonomically organized, overview of the literature on the topic (Chapter 3). We then organize, summarize and integrate the efforts of our own group on developing solutions for the problem that have historically produced state-of-the-art (by the time of their proposals) results in terms of the quality of the disambiguation results. Thus, Chapter 4 covers HHC - Heuristic-based Clustering, an author name disambiguation method that is based on two specific real-world assumptions regarding scientific authorship. Then, Chapter 5 describes SAND - Self-training Author Name Disambiguator and Chapter 6 presents two incremental author name disambiguation methods, namely INDi - Incremental Unsupervised Name Disambiguation and INC- Incremental Nearest Cluster. Finally, Chapter 7 provides an overview of recent author name disambiguation methods that address new specific approaches such as graph-based representations, alternative predefined similarity functions, visualization facilities and approaches based on artificial neural networks. The chapters are followed by three appendices that cover, respectively: (i) a pattern matching function for comparing proper names and used by some of the methods addressed in this book; (ii) a tool for generating synthetic collections of citation records for distinct experimental tasks; and (iii) a number of datasets commonly used to evaluate author name disambiguation methods. In summary, the book organizes a large body of knowledge and work in the area of author name disambiguation in the last decade, hoping to consolidate a solid basis for future developments in the field.
(source: Nielsen Book Data)

• Preface Acknowledgments Introduction Data Collection Data-Driven Compatibility Modeling Knowledge-Guided Compatibility Modeling Prototype-Wise Interpretable Compatibility Modeling Personalized Compatibility Modeling Personalized Capsule Wardrobe Creation Research Frontiers Bibliography Authors' Biographies.
• (source: Nielsen Book Data)

Nowadays, fashion has become an essential aspect of people's daily life. As each outfit usually comprises several complementary items, such as a top, bottom, shoes, and accessories, a proper outfit largely relies on the harmonious matching of these items. Nevertheless, not everyone is good at outfit composition, especially those who have a poor fashion aesthetic. Fortunately, in recent years the number of online fashion-oriented communities, like IQON and Chictopia, as well as e-commerce sites, like Amazon and eBay, has grown. The tremendous amount of real-world data regarding people's various fashion behaviors has opened a door to automatic clothing matching. Despite its significant value, compatibility modeling for clothing matching that assesses the compatibility score for a given set of (equal or more than two) fashion items, e.g., a blouse and a skirt, yields tough challenges: (a) the absence of comprehensive benchmark; (b) comprehensive compatibility modeling with the multi-modal feature variables is largely untapped; (c) how to utilize the domain knowledge to guide the machine learning; (d) how to enhance the interpretability of the compatibility modeling; and (e) how to model the user factor in the personalized compatibility modeling. These challenges have been largely unexplored to date. In this book, we shed light on several state-of-the-art theories on compatibility modeling. In particular, to facilitate the research, we first build three large-scale benchmark datasets from different online fashion websites, including IQON and Amazon. We then introduce a general data-driven compatibility modeling scheme based on advanced neural networks. To make use of the abundant fashion domain knowledge, i.e., clothing matching rules, we next present a novel knowledge guided compatibility modeling framework. Thereafter, to enhance the model interpretability, we put forward a prototype wise interpretable compatibility modeling approach. Following that, noticing the subjective aesthetics of users, we extend the general compatibility modeling to the personalized version. Moreover, we further study the real-world problem of personalized capsule wardrobe creation, aiming to generate a minimum collection of garments that is both compatible and suitable for the user. Finally, we conclude the book and present future research directions, such as the generative compatibility modeling, virtual try-on with arbitrary poses, and clothing generation.
(source: Nielsen Book Data)

• Acknowledgments Symbols Introduction Evaluation Approaches Modeling Document Lengths Modeling Word Frequencies, Assuming Independence Modeling Term Dependence Modeling Word Strings Models of Corpus Growth Generation of Compatible Queries Proof of the Simulation Pudding Speed of Operation Leaking Confidential Information Discussion Conclusions Bibliography Authors' Biographies.
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Simulated test collections may find application in situations where real datasets cannot easily be accessed due to confidentiality concerns or practical inconvenience. They can potentially support Information Retrieval (IR) experimentation, tuning, validation, performance prediction, and hardware sizing. Naturally, the accuracy and usefulness of results obtained from a simulation depend upon the fidelity and generality of the models which underpin it. The fidelity of emulation of a real corpus is likely to be limited by the requirement that confidential information in the real corpus should not be able to be extracted from the emulated version. We present a range of methods exploring trade-offs between emulation fidelity and degree of preservation of privacy. We present three different simple types of text generator which work at a micro level: Markov models, neural net models, and substitution ciphers. We also describe macro level methods where we can engineer macro properties of a corpus, giving a range of models for each of the salient properties: document length distribution, word frequency distribution (for independent and non-independent cases), word length and textual representation, and corpus growth. We present results of emulating existing corpora and for scaling up corpora by two orders of magnitude. We show that simulated collections generated with relatively simple methods are suitable for some purposes and can be generated very quickly. Indeed it may sometimes be feasible to embed a simple lightweight corpus generator into an indexer for the purpose of efficiency studies. Naturally, a corpus of artificial text cannot support IR experimentation in the absence of a set of compatible queries. We discuss and experiment with published methods for query generation and query log emulation. We present a proof-of-the-pudding study in which we observe the predictive accuracy of efficiency and effectiveness results obtained on emulated versions of TREC corpora. The study includes three open-source retrieval systems and several TREC datasets. There is a trade-off between confidentiality and prediction accuracy and there are interesting interactions between retrieval systems and datasets. Our tentative conclusion is that there are emulation methods which achieve useful prediction accuracy while providing a level of confidentiality adequate for many applications.
(source: Nielsen Book Data)

• Acknowledgments Introduction How Search Engines Work How Searchers Think About Search How Measurement Works Components of Search that Can Be Evaluated Units of Analysis: Query, Task, User Types of Metrics for Search Evaluation What Gets Left Out: Understanding References Author's Biography.
• (source: Nielsen Book Data)

This book is intended for anyone interested in learning more about how search works and how it is evaluated. We all use search-it's a familiar utility. Yet, few of us stop and think about how search works, what makes search results good, and who, if anyone, decides what good looks like. Search has a long and glorious history, yet it continues to evolve, and with it, the measurement and our understanding of the kinds of experiences search can deliver continues to evolve, as well. We will discuss the basics of how search engines work, how humans use search engines, and how measurement works. Equipped with these general topics, we will then dive into the established ways of measuring search user experience, and their pros and cons. We will talk about collecting labels from human judges, analyzing usage logs, surveying end users, and even touch upon automated evaluation methods. After introducing different ways of collecting metrics, we will cover experimentation as it applies to search evaluation. The book will cover evaluating different aspects of search-from search user interface (UI), to results presentation, to the quality of search algorithms. In covering these topics, we will touch upon many issues in evaluation that became sources of controversy-from user privacy, to ethical considerations, to transparency, to potential for bias. We will conclude by contrasting measuring with understanding, and pondering the future of search evaluation.
(source: Nielsen Book Data)

• 1. Introduction
• 1.1. Background : user study in IIR research
• 1.2. Aim of the work
• 1.3. Systematic review and user study evaluation
• 1.4. Implications of the faceted framework

• 2. Interactive information retrieval
• 2.1. Background
• 2.2. Understanding user behavior and experience
• 2.3. System/interface features evaluation
• 2.4. Meta-evaluation of evaluation metrics
• 2.5. Summary

• 3. Methodology : paper selection and coding scheme
• 3.1. Paper selection : SIGIR, CHIIR, CHI, JASIST, IP & M, and TOIS
• 3.2. Coding scheme

• 4. Faceted framework of IIR user studies
• 4.1. Structure of the faceted framework
• 4.2. Under-reported facets and factors
• 4.3. Summary

• 5. Evaluating IIR user studies of different types
• 5.1. Understanding user behavior and experience
• 5.2. System/interface features evaluation
• 5.3. Meta-evaluation of evaluation measures
• 5.4. Summary

• 6. Implications and limitations of the faceted framework
• 6.1. Implications for interaction IR user study design and evaluation
• 6.2. Implications for IIR user study reporting practices
• 6.3. Limitations and challenges
• 7. Conclusion and future directions.

Since user study design has been widely applied in search interactions and information retrieval (IR) systems evaluation studies, a deep reflection and meta-evaluation of interactive IR (IIR) user studies is critical for sharpening the instruments of IIR research and improving the reliability and validity of the conclusions drawn from IIR user studies. To this end, we developed a faceted framework for supporting user study design, reporting, and evaluation based on a systematic review of the state-of-the-art IIR research papers recently published in several top IR venues (n=462). Within the framework, we identify three major types of research focuses, extract and summarize facet values from specific cases, and highlight the under-reported user study components which may significantly affect the results of research. Then, we employ the faceted framework in evaluating a series of IIR user studies against their respective research questions and explain the roles and impacts of the underlying connections and "collaborations" among different facet values. Through bridging diverse combinations of facet values with the study design decisions made for addressing research problems, the faceted framework can shed light on IIR user study design, reporting, and evaluation practices and help students and young researchers design and assess their own studies

• Preface Acknowledgments Introduction Designing and Developing Microtasks Quality Assurance Algorithms and Techniques for Quality Control The Human Side of Human Computation Putting All Things Together Systems and Data Pipelines Looking Ahead Bibliography Author's Biography.
• (source: Nielsen Book Data)

Many data-intensive applications that use machine learning or artificial intelligence techniques depend on humans providing the initial dataset, enabling algorithms to process the rest or for other humans to evaluate the performance of such algorithms. Not only can labeled data for training and evaluation be collected faster, cheaper, and easier than ever before, but we now see the emergence of hybrid human-machine software that combines computations performed by humans and machines in conjunction. There are, however, real-world practical issues with the adoption of human computation and crowdsourcing. Building systems and data processing pipelines that require crowd computing remains difficult. In this book, we present practical considerations for designing and implementing tasks that require the use of humans and machines in combination with the goal of producing high-quality labels.
(source: Nielsen Book Data)

• Preface Acknowledgments Introduction Five Stories to a Model of Video Structure Coda: Provocations on Filmic Retrieval, Hunting, Meandering, and Browsing Bibliography Author Biographies.
• (source: Nielsen Book Data)

For over a century, motion pictures have entertained us, occasionally educated us, and even served a few specialized fields of study. Now, however, with the precipitous drop in prices and increase in image quality, motion pictures are as widespread as paperback books and postcards once were. Yet, theories and practices of analysis for particular genres and analytical stances, definitions, concepts, and tools that span platforms have been wanting. Therefore, we developed a suite of tools to enable close structural analysis of the time-varying signal set of a movie. We take an information-theoretic approach (message is a signal set) generated (coded) under various antecedents (sent over some channel) decoded under some other set of antecedents. Cultural, technical, and personal antecedents might favor certain message-making systems over others. The same holds true at the recipient end-yet, the signal set remains the signal set. In order to discover how movies work-their structure and meaning-we honed ways to provide pixel level analysis, forms of clustering, and precise descriptions of what parts of a signal influence viewer behavior. We assert that analysis of the signal set across the evolution of film-from Edison to Hollywood to Brakhage to cats on social media-yields a common ontology with instantiations (responses to changes in coding and decoding antecedents).
(source: Nielsen Book Data)

• Preface Acknowledgments Abbreviations Introduction and Iconic Language for Images Literature Review: State of the Art Natural Scene Perception and Eye Tracking Trends in Handling Future Image Collections Bibliography Author Biographies.
• (source: Nielsen Book Data)

This book focuses on the methodologies, organization, and communication of digital image collection research that utilizes social media content. ("Image" is here understood as a cultural, conventional, and commercial-stock photo-representation.) The lecture offers expert views that provide different interpretations of images and their potential implementations. Linguistic and semiotic methodologies as well as eye-tracking research are employed to both analyze images and comprehend how humans consider them, including which salient features generally attract viewers' attention. This literature review covers image-specifically photographic-research since 2005, when major social media platforms emerged. A citation analysis includes an overview of co-citation maps that demonstrate the nexus of image research literature and the journals in which they appear. Eye tracking tests whether scholarly templates focus on the proper features of an image, such as people, objects, time, etc., and if a prescribed theme affects the eye movements of the observer. The results may point to renewed requirements for building image search engines. As it stands, image management already requires new algorithms and a new understanding that involves text recognition and very large database processing. The aim of this book is to present different image research areas and demonstrate the challenges image research faces. The book's scope is, by necessity, far from comprehensive, since the field of digital image research does not cover fake news, image manipulation, mobile photos, etc.; these issues are very complex and need a publication of their own. This book should primarily be useful for students in library and information science, psychology, and computer science.
(source: Nielsen Book Data)

• Introduction Evaluating Indicators Usage, Popularity and Attention Indicators Academic, Commercial and Organizational Web Indicators Educational Impact Indicators Medical Impact Indicators Applications Collecting Data for Sets of Documents Indicator Formulae and Experimental Considerations Calculating Indicators with Webometric Analyst Conclusions Bibliography Author Biography.
• (source: Nielsen Book Data)

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.
(source: Nielsen Book Data)

• 10. Group information management and the social fabric of PIM
• 10.1 We are not alone
• 10.1.1 Who is the group?
• 10.1.2 It's about the information
• 10.1.3 Information to represent (and five other "R"s as well)
• 10.1.4 Information to communicate
• 10.1.5 Information to know
• 10.2 Social situations of PIM
• 10.2.1 @Home
• 10.2.2 @School
• 10.2.3 @Work
• 10.2.4 @Play (informal, social, sexual)
• 10.2.5 @Large
• 10.3 Conclusions: information, communication and a never-ending dialog to know.

• 11. PIM by design
• 11.1 Taking stock. What's been covered? What remains?
• 11.1.1 Getting better than the given
• 11.1.2 Learning from human-computer interaction
• 11.1.3 A plan for the remainder of this chapter
• 11.2 Considerations when designing tools for PIM
• 11.2.1 It's about the information
• 11.2.2 We are not machines: don't overlook the social and the emotional in PIM
• 11.2.3 "Too many information!" Don't make information fragmentation worse than it is already
• 11.2.4 Do cover the "checklists" of PIM
• 11.3 Toward practical methods with practical impact
• 11.3.1 A few studies of PIM and the methods they have used
• 11.3.2 Some results, their implications, and their impact
• 11.3.3 What about cognitive modeling?
• 11.3.4 Mixed methods made "massive open online"
• 11.4 Big challenges, big opportunities in PIM design
• 11.4.1 Putting more "meta" into a practice of PIM
• 11.4.2 Storytelling to weave personal information together
• 11.5 Conclusions: PIM on purpose.

• 12. To each of us, our owN
• 12.1 A clarification of terms
• 12.2 Considerations when designing your practice of PIM
• 12.2.1 It's about your information
• 12.2.2 Don't think you're so special (even if you are a little different)
• 12.2.3 Do begin to bring your information together
• 12.2.4 Cover checklists as these make sense for your practice
• 12.2.5 Consider adopting some of the "recommended" practices of PIM (if you haven't done so already)
• 12.3 Methods you might use as you design your own practice of PIM
• 12.4 Some ways to challenge yourself in your practice of PIM
• 12.5 Conclusions: building a better world.

Focus in this concluding Part 3 to the series shifts to the practical and to the near future. What can we do, now or soon, to manage our information better? And, as we do so, how might we build a better world? Part 3 is in three chapters: "Group Information Management and the Social Fabric in PIM." How do we preserve and promote our PIM practices as we interact with others at home, at school, at work, at play and in wider, even global, communities? (Chapter 10). "PIM by Design." What principles guide us? How can developers build better tools for PIM? How can the rest of us make better use of the tools we already have? (Chapter 11). "To Each of Us, Our Own" concludes with an exploration of the ways each of us, individually, can develop better practices for the management of our information in service of the lives we wish to live and toward a better world we all must share (Chapter 12).

• Introduction Existing Research University of Maryland's Children and Internet Search Studies and the Search Role Framework Roles of Reaction: Developing and Non-Motivated Searchers Roles of Preference: Rule-Bound, Domain-Specific, and Visual Searchers Roles of Proficiency" Power and Social Searchers Conclusions Acknowledgments References Author Biographies.
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Searching the Internet and the ability to competently use search engines are increasingly becoming an important part of children's daily lives. Whether mobile or at home, children use search interfaces to explore personal interests, complete academic assignments, and have social interaction. However, engaging with search also means engaging with an ever-changing and evolving search landscape. There are continual software updates, multiple devices used to search (e.g., phones, tablets), an increasing use of social media, and constantly updated Internet content. For young searchers, this can require infinite adaptability or mean being hopelessly confused. This book offers a perspective centered on children's search experiences as a whole instead of thinking of search as a process with separate and potentially problematic steps. Reading the prior literature with a child-centered view of search reveals that children have been remarkably consistent over time as searchers, displaying the same search strategies regardless of the landscape of search. However, no research has synthesized these consistent patterns in children's search across the literature, and only recently have these patterns been uncovered as distinct search roles, or searcher types. Based on a four-year longitudinal study on children's search experiences, this book weaves together the disparate evidence in the literature through the use of 9 search roles for children ages 7-15. The search role framework has a distinct advantage because it encourages adult stakeholders to design children's search tools to support and educate children at their existing levels of search strength and deficit, rather than expecting children to adapt to a transient search landscape.
(source: Nielsen Book Data)

• Preface Acknowledgments Figure Credits Introduction Reading Through the Ages Key Concepts Lightweight Interactions Improving Digital Reading Bibliography Authors' Biographies.
• (source: Nielsen Book Data)

Reading is a complex human activity that has evolved, and co-evolved, with technology over thousands of years. Mass printing in the fifteenth century firmly established what we know as the modern book, with its physical format of covers and paper pages, and now-standard features such as page numbers, footnotes, and diagrams. Today, electronic documents are enabling paperless reading supported by eReading technologies such as Kindles and Nooks, yet a high proportion of users still opt to print on paper before reading. This persistent habit of "printing to read" is one sign of the shortcomings of digital documents -- although the popularity of eReaders is one sign of the shortcomings of paper. How do we get the best of both worlds? The physical properties of paper (for example, it is light, thin, and flexible) contribute to the ease with which physical documents are manipulated; but these properties have a completely different set of affordances to their digital equivalents. Paper can be folded, ripped, or scribbled on almost subconsciously -- activities that require significant cognitive attention in their digital form, if they are even possible. The nearly subliminal interaction that comes from years of learned behavior with paper has been described as lightweight interaction, which is achieved when a person actively reads an article in a way that is so easy and unselfconscious that they are not apt to remember their actions later. Reading is now in a period of rapid change, and digital text is fast becoming the predominant mode of reading. As a society, we are merely at the start of the journey of designing truly effective tools for handling digital text. This book investigates the advantages of paper, how the affordances of paper can be realized in digital form, and what forms best support lightweight interaction for active reading. To understand how to design for the future, we review the ways reading technology and reader behavior have both changed and remained constant over hundreds of years. We explore the reasoning behind reader behavior and introduce and evaluate several user interface designs that implement these lightweight properties familiar from our everyday use of paper. We start by looking back, reviewing the development of reading technology and the progress of research on reading over many years. Drawing key concepts from this review, we move forward to develop and test methods for creating new and more effective interactions for supporting digital reading. Finally, we lay down a set of lightweight attributes which can be used as evidence-based guidelines to improve the usability of future digital reading technologies. By the end of this book, then, we hope you will be equipped to critique the present state of digital reading, and to better design and evaluate new interaction styles and technologies.
(source: Nielsen Book Data)