Bøger i Synthesis Lectures on Information Concepts, Retrieval, and Services serien

Genealogies document relationships between persons involved in historical events. Information about the events is parsed from communications from the past. This book explores a way to organize information from multiple communications into a trustworthy representation of a genealogical history of the modern world. The approach defines metrics for evaluating the consistency, correctness, closure, connectivity, completeness, and coherence of a genealogy. The metrics are evaluated using a 312,000-person research genealogy that explores the common ancestors of the royal families of Europe. A major result is that completeness is defined by a genealogy symmetry property driven by two exponential processes, the doubling of the number of potential ancestors each generation, and the rapid growth of lineage coalescence when the number of potential ancestors exceeds the available population. A genealogy expands from an initial root person to a large number of lineages, which then coalesce into a small number of progenitors. Using the research genealogy, candidate progenitors for persons of Western European descent are identified. A unifying ancestry is defined to which historically notable persons can be linked.

This book explores the connections between qualitative data reuse, big social research, and data curation. A review of existing literature identifies the key issues of context, data quality and trustworthiness, data comparability, informed consent, privacy and confidentiality, and intellectual property and data ownership. Through interviews of qualitative researchers, big social researchers, and data curators, the author further examines each key issue and produces new insights about how domain differences affect each community of practice¿s viewpoints, different strategies that researchers and curators use to ensure responsible practice, and different perspectives on data curation. The book suggests that encouraging connections between qualitative researchers, big social researchers, and data curators can support responsible scaling up of social research, thus enhancing discoveries in social and behavioral science.

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.

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.  

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.

This book systemically presents key concepts of multi-modal hashing technology, recent advances on large-scale efficient multimedia search and recommendation, and recent achievements in multimedia indexing technology. With the explosive growth of multimedia contents, multimedia retrieval is currently facing unprecedented challenges in both storage cost and retrieval speed. The multi-modal hashing technique can project high-dimensional data into compact binary hash codes. With it, the most time-consuming semantic similarity computation during the multimedia retrieval process can be significantly accelerated with fast Hamming distance computation, and meanwhile the storage cost can be reduced greatly by the binary embedding. The authors introduce the categorization of existing multi-modal hashing methods according to various metrics and datasets. The authors also collect recent multi-modal hashing techniques and describe the motivation, objective formulations, and optimization steps for context-aware hashing methods based on the tag-semantics transfer.

This book explores the rich history of the keyword from its earliest manifestations (long before it appeared anywhere in Google Trends or library cataloging textbooks) in order to illustrate its implicit and explicit mediation of human cognition and communication processes. The author covers the concept of the keyword from its deictic origins in primate and proto-speech communities, through its development within oral traditions, to its initial appearances in numerous graphical forms and its workings over time within a variety of indexing traditions and technologies. The book follows the history all the way to its role in search engine optimization and social media strategies and its potential as an element in the slowly emerging semantic web, as well as in multiple voice search applications. The author synthesizes different perspectives on the significance of this often-invisible intermediary, both in and out of the library and information science context, helping readers to understand how it has come to be so embedded in our daily life.This book: Provides a thorough history of the keyword, from primate and proto-speech communities to current timesExplains how the concept of the keyword relates to human cognition and communication processesHighlights the applications of the keyword, both in and out of the library and information science context

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.

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.

This book seeks to advance our understanding of the relationship between information and human values by synthesizing the complementary but typically disconnected threads in the literature, reflecting on my 15 years of research on the relationship between information and human values, advancing our intellectual understanding of the key facets of this topic, and encouraging further research to continue exploring this important and timely research topic. The book begins with an explanation of what human values are and why they are important. Next, three distinct literatures on values, information, and technology are analyzed and synthesized, including the social psychology literature on human values, the information studies literature on the core values of librarianship, and the human-computer interaction literature on value-sensitive design. After that, three detailed case studies are presented based on reflections on a wide range of research studies. The first case study focuses on the role of human values in the design and use of educational simulations. The second case study focuses on the role of human values in the design and use of computational models. The final case study explores human values in communication via, about, or using information technology. The book concludes by laying out a values and design cycle for studying values in information and presenting an agenda for further research.

Research on multiculturalism and information and communication technology (ICT) has been important to understanding recent history, planning for future large-scale initiatives, and understanding unrealized expectations for social and technological change. This interdisciplinary area of research has examined interactions between ICT and culture at the group and society levels. However, there is debate within the literature as to the nature of the relationship between culture and technology. In this synthesis, we suggest that the tensions result from the competing ideologies that drive researchers, allowing us to conceptualize the relationship between culture and ICT under three primary models, each with its own assumptions: 1) Social informatics, 2) Social determinism, and 3) Technological determinism. Social informatics views the relationship to be one of sociotechnical interaction, in which culture and ICTs affect each other mutually and iteratively, rather than linearly; the vast majority of the literature approach the relationships between ICT and culture under the assumptions of social informatics. From a socially deterministic perspective, ICTs are viewed as the dependent variable in the equation, whereas, from a technologically deterministic perspective, ICTs are an independent variable. The issues of multiculturalism and ICTs attracted much scholarly attention and have been explored under a myriad of contexts, with substantial literature on global development, social and political issues, business and public administration as well as education and scholarly collaboration. We synthesize here research in the areas of global development, social and political issues, and business collaboration. Finally we conclude by proposing under-explored areas for future research directions.

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 "e;printing to read"e; 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.

With its theme, "e;Our Information, Always and Forever,"e; Part I of this book covers the basics of personal information management (PIM) including six essential activities of PIM and six (different) ways in which information can be personal to us. Part I then goes on to explore key issues that arise in the "e;great migration"e; of our information onto the Web and into a myriad of mobile devices. Part 2 provides a more focused look at technologies for managing information that promise to profoundly alter our practices of PIM and, through these practices, the way we lead our lives. Part 2 is in five chapters:- Chapter 5. Technologies of Input and Output. Technologies in support of gesture, touch, voice, and even eye movements combine to support a more natural user interface (NUI). Technologies of output include glasses and "e;watch"e; watches. Output will also increasingly be animated with options to "e;zoom"e;. - Chapter 6. Technologies to Save Our Information. We can opt for "e;life logs"e; to record our experiences with increasing fidelity. What will we use these logs for? And what isn't recorded that should be?- Chapter 7. Technologies to Search Our Information. The potential for personalized search is enormous and mostly yet to be realized. Persistent searches, situated in our information landscape, will allow us to maintain a diversity of projects and areas of interest without a need to continually switch from one to another to handle incoming information. - Chapter 8. Technologies to Structure Our Information. Structure is key if we are to keep, find, and make effective use of our information. But how best to structure? And how best to share structured information between the applications we use, with other people, and also with ourselves over time? What lessons can we draw from the failures and successes in web-based efforts to share structure?- Chapter 9. PIM Transformed and Transforming: Stories from the Past, Present and Future. Part 2 concludes with a comparison between Licklider's world of information in 1957 and our own world of information today. And then we consider what the world of information is likely to look like in 2057. Licklider estimated that he spent 85% of his "e;thinking time"e; in activities that were clerical and mechanical and might (someday) be delegated to the computer. What percentage of our own time is spent with the clerical and mechanical? What about in 2057?

Information Retrieval (IR) models are a core component of IR research and IR systems. The past decade brought a consolidation of the family of IR models, which by 2000 consisted of relatively isolated views on TF-IDF (Term-Frequency times Inverse-Document-Frequency) as the weighting scheme in the vector-space model (VSM), the probabilistic relevance framework (PRF), the binary independence retrieval (BIR) model, BM25 (Best-Match Version 25, the main instantiation of the PRF/BIR), and language modelling (LM). Also, the early 2000s saw the arrival of divergence from randomness (DFR). Regarding intuition and simplicity, though LM is clear from a probabilistic point of view, several people stated: "e;It is easy to understand TF-IDF and BM25. For LM, however, we understand the math, but we do not fully understand why it works."e;This book takes a horizontal approach gathering the foundations of TF-IDF, PRF, BIR, Poisson, BM25, LM, probabilistic inference networks (PIN's), and divergence-based models. The aim is to create a consolidated and balanced view on the main models. A particular focus of this book is on the "e;relationships between models."e; This includes an overview over the main frameworks (PRF, logical IR, VSM, generalized VSM) and a pairing of TF-IDF with other models. It becomes evident that TF-IDF and LM measure the same, namely the dependence (overlap) between document and query. The Poisson probability helps to establish probabilistic, non-heuristic roots for TF-IDF, and the Poisson parameter, average term frequency, is a binding link between several retrieval models and model parameters. Table of Contents: List of Figures / Preface / Acknowledgments / Introduction / Foundations of IR Models / Relationships Between IR Models / Summary & Research Outlook / Bibliography / Author's Biography / Index

Society faces many challenges in workplaces, everyday life situations, and education contexts. Within information behavior research, there are often calls to bridge inclusiveness and for greater collaboration, with user-centered design approaches and, more specifically, participatory design practices. Collaboration and participation are essential in addressing contemporary societal challenges, designing creative information objects and processes, as well as developing spaces for learning, and information and research interventions. The intention is to improve access to information and the benefits to be gained from that. This also applies to bridging the digital divide and for embracing artificial intelligence. With regard to research and practices within information behavior, it is crucial to consider that all users should be involved. Many information activities (i.e., activities falling under the umbrella terms of information behavior and information practices) manifest through participation, and thus, methods such as participatory design may help unfold both information behavior and practices as well as the creation of information objects, new models, and theories. Information sharing is one of its core activities. For participatory design with its value set of democratic, inclusive, and open participation towards innovative practices in a diversity of contexts, it is essential to understand how information activities such as sharing manifest itself. For information behavior studies it is essential to deepen understanding of how information sharing manifests in order to improve access to information and the use of information. Third Space is a physical, virtual, cognitive, and conceptual space where participants may negotiate, reflect, and form new knowledge and worldviews working toward creative, practical and applicable solutions, finding innovative, appropriate research methods, interpreting findings, proposing new theories, recommending next steps, and even designing solutions such as new information objects or services. Information sharing in participatory design manifests in tandem with many other information interaction activities and especially information and cognitive processing. Although there are practices of individual information sharing and information encountering, information sharing mostly relates to collaborative information behavior practices, creativity, and collective decision-making. Our purpose with this book is to enable students, researchers, and practitioners within a multi-disciplinary research field, including information studies and Human-Computer Interaction approaches, to gain a deeper understanding of how the core activity of information sharing in participatory design, in which Third Space may be a platform for information interaction, is taking place when using methods utilized in participatory design to address contemporary societal challenges. This could also apply for information behavior studies using participatory design as methodology. We elaborate interpretations of core concepts such as participatory design, Third Space, information sharing, and collaborative information behavior, before discussing participatory design methods and processes in more depth. We also touch on information behavior, information practice, and other important concepts. Third Space, information sharing, and information interaction are discussed in some detail. A framework, with Third Space as a core intersecting zone, platform, and adaptive and creative space to study information sharing and other information behavior and interactions are suggested. As a tool to envision information behavior and suggest future practices, participatory design serves as a set of methods and tools in which new interpretations of the design of information behavior studies and eventually new information objects are being initiated involving multiple stakeholders in future information landscapes. For this purpose, we argue that Third Space can be used as an intersection zone to study information sharing and other information activities, but more importantly it can serve as a Third Space Information Behavior (TSIB) study framework where participatory design methodology and processes are applied to information behavior research studies and applications such as information objects, systems, and services with recognition of the importance of situated awareness.

While great strides have been made in the field of search and recommendation, there are still challenges and opportunities to address information access issues that involve solving tasks and accomplishing goals for a wide variety of users. Specifically, we lack intelligent systems that can detect not only the request an individual is making (what), but also understand and utilize the intention (why) and strategies (how) while providing information and enabling task completion. Many scholars in the fields of information retrieval, recommender systems, productivity (especially in task management and time management), and artificial intelligence have recognized the importance of extracting and understanding people's tasks and the intentions behind performing those tasks in order to serve them better. However, we are still struggling to support them in task completion, e.g., in search and assistance, and it has been challenging to move beyond single-query or single-turn interactions. The proliferation of intelligent agents has unlocked new modalities for interacting with information, but these agents will need to be able to work understanding current and future contexts and assist users at task level. This book will focus on task intelligence in the context of search and recommendation. Chapter 1 introduces readers to the issues of detecting, understanding, and using task and task-related information in an information episode (with or without active searching). This is followed by presenting several prominent ideas and frameworks about how tasks are conceptualized and represented in Chapter 2. In Chapter 3, the narrative moves to showing how task type relates to user behaviors and search intentions. A task can be explicitly expressed in some cases, such as in a to-do application, but often it is unexpressed. Chapter 4 covers these two scenarios with several related works and case studies. Chapter 5 shows how task knowledge and task models can contribute to addressing emerging retrieval and recommendation problems. Chapter 6 covers evaluation methodologies and metrics for task-based systems, with relevant case studies to demonstrate their uses. Finally, the book concludes in Chapter 7, with ideas for future directions in this important research area.

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. 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.

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.Many of the methods described here have been implemented in the open source project SynthaCorpus, accessible at: https://bitbucket.org/davidhawking/synthacorpus/

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.

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).

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 "e;collaborations"e; 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.

Information Retrieval performance measures are usually retrospective in nature, representing the effectiveness of an experimental process. However, in the sciences, phenomena may be predicted, given parameter values of the system. After developing a measure that can be applied retrospectively or can be predicted, performance of a system using a single term can be predicted given several different types of probabilistic distributions. Information Retrieval performance can be predicted with multiple terms, where statistical dependence between terms exists and is understood. These predictive models may be applied to realistic problems, and then the results may be used to validate the accuracy of the methods used. The application of metadata or index labels can be used to determine whether or not these features should be used in particular cases. Linguistic information, such as part-of-speech tag information, can increase the discrimination value of existing terminology and can be studied predictively.This work provides methods for measuring performance that may be used predictively. Means of predicting these performance measures are provided, both for the simple case of a single term in the query and for multiple terms. Methods of applying these formulae are also suggested.

As digital collections continue to grow, the underlying technologies to serve up content also continue to expand and develop. As such, new challenges are presented which continue to test ethical ideologies in everyday environs of the practitioner. There are currently no solid guidelines or overarching codes of ethics to address such issues. The digitization of modern archival collections, in particular, presents interesting conundrums when factors of privacy are weighed and reviewed in both small and mass digitization initiatives. Ethical decision making needs to be present at the onset of project planning in digital projects of all sizes, and we also need to identify the role and responsibility of the practitioner to make more virtuous decisions on behalf of those with no voice or awareness of potential privacy breaches.In this book, notions of what constitutes private information are discussed, as is the potential presence of such information in both analog and digital collections. This book lays groundwork to introduce the topic of privacy within digital collections by providing some examples from documented real-world scenarios and making recommendations for future research.A discussion of the notion privacy as concept will be included, as well as some historical perspective (with perhaps one the most cited work on this topic, for example, Warren and Brandeis' "e;Right to Privacy,"e; 1890). Concepts from the The Right to Be Forgotten case in 2014 (Google Spain SL, Google Inc. v Agencia Espala de Proteccin de Datos, Mario Costeja Gonzlez) are discussed as to how some lessons may be drawn from the response in Europe and also how European data privacy laws have been applied. The European ideologies are contrasted with the Right to Free Speech in the First Amendment in the U.S., highlighting the complexities in setting guidelines and practices revolving around privacy issues when applied to real life scenarios. Two ethical theories are explored: Consequentialism and Deontological. Finally, ethical decision making models will also be applied to our framework of digital collections. Three case studies are presented to illustrate how privacy can be defined within digital collections in some real-world examples.

With the rapid development of mobile Internet and smart personal devices in recent years, mobile search has gradually emerged as a key method with which users seek online information. In addition, cross-device search also has been regarded recently as an important research topic. As more mobile applications (APPs) integrate search functions, a user's mobile search behavior on different APPs becomes more significant. This book provides a systematic review of current mobile search analysis and studies user mobile search behavior from several perspectives, including mobile search context, APP usage, and different devices. Two different user experiments to collect user behavior data were conducted. Then, through the data from user mobile phone usage logs in natural settings, we analyze the mobile search strategies employed and offer a context-based mobile search task collection, which then can be used to evaluate the mobile search engine. In addition, we combine mobile search with APP usage to give more in-depth analysis, such as APP transition in mobile search and follow-up actions triggered by mobile search. The study, combining the mobile search with APP usage, can contribute to the interaction design of APPs, such as the search recommendation and APP recommendation. Addressing the phenomenon of users owning more smart devices today than ever before, we focus on user cross device search behavior. We model the information preparation behavior and information resumption behavior in cross-device search and evaluate the search performance in cross-device search. Research on mobile search behaviors across different devices can help to understand online user information behavior comprehensively and help users resume their search tasks on different devices.

The field of human information behavior runs the gamut of processes from the realization of a need or gap in understanding, to the search for information from one or more sources to fill that gap, to the use of that information to complete a task at hand or to satisfy a curiosity, as well as other behaviors such as avoiding information or finding information serendipitously. Designers of mechanisms, tools, and computer-based systems to facilitate this seeking and search process often lack a full knowledge of the context surrounding the search. This context may vary depending on the job or role of the person; individual characteristics such as personality, domain knowledge, age, gender, perception of self, etc.; the task at hand; the source and the channel and their degree of accessibility and usability; and the relationship that the seeker shares with the source. Yet researchers have yet to agree on what context really means. While there have been various research studies incorporating context, and biennial conferences on context in information behavior, there lacks a clear definition of what context is, what its boundaries are, and what elements and variables comprise context.In this book, we look at the many definitions of and the theoretical and empirical studies on context, and I attempt to map the conceptual space of context in information behavior. I propose theoretical frameworks to map the boundaries, elements, and variables of context. I then discuss how to incorporate these frameworks and variables in the design of research studies on context. We then arrive at a unified definition of context. This book should provide designers of search systems a better understanding of context as they seek to meet the needs and demands of information seekers. It will be an important resource for researchers in Library and Information Science, especially doctoral students looking for one resource that covers an exhaustive range of the most current literature related to context, the best selection of classics, and a synthesis of these into theoretical frameworks and a unified definition. The book should help to move forward research in the field by clarifying the elements, variables, and views that are pertinent. In particular, the list of elements to be considered, and the variables associated with each element will be extremely useful to researchers wanting to include the influences of context in their studies.

Chance, luck, and good fortune are the usual go-to descriptors of serendipity, a phenomenon aptly often coupled with famous anecdotes of accidental discoveries in engineering and science in modern history such as penicillin, Teflon, and Post-it notes. Serendipity, however, is evident in many fields of research, in organizations, in everyday life-and there is more to it than luck implies. While the phenomenon is strongly associated with in person interactions with people, places, and things, most attention of late has focused on its preservation and facilitation within digital information environments. Serendipity's association with unexpected, positive user experiences and outcomes has spurred an interest in understanding both how current digital information environments support serendipity and how novel approaches may be developed to facilitate it. Research has sought to understand serendipity, how it is manifested in people's personality traits and behaviors, how it may be facilitated in digital information environments such as mobile applications, and its impacts on an individual, an organizational, and a wider level. Because serendipity is expressed and understood in different ways in different contexts, multiple methods have been used to study the phenomenon and evaluate digital information environments that may support it. This volume brings together different disciplinary perspectives and examines the motivations for studying serendipity, the various ways in which serendipity has been approached in the research, methodological approaches to build theory, and how it may be facilitated. Finally, a roadmap for serendipity research is drawn by integrating key points from this volume to produce a framework for the examination of serendipity in digital information environments.

Public health thrives on high-quality evidence, yet acquiring meaningful data on a population remains a central challenge of public health research and practice. Social monitoring, the analysis of social media and other user-generated web data, has brought advances in the way we leverage population data to understand health. Social media offers advantages over traditional data sources, including real-time data availability, ease of access, and reduced cost. Social media allows us to ask, and answer, questions we never thought possible.This book presents an overview of the progress on uses of social monitoring to study public health over the past decade. We explain available data sources, common methods, and survey research on social monitoring in a wide range of public health areas. Our examples come from topics such as disease surveillance, behavioral medicine, and mental health, among others. We explore the limitations and concerns of these methods. Our survey of this exciting new field of data-driven research lays out future research directions.