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Can there be reliable information that is also relevant to decision making? Information for Efficient Decision Making: Big Data, Blockchain and Relevance focuses on the consolidation of information to facilitate making decisions in firms, in order to make their operations efficient to reduce their costs and consequently, increase their profitability. The advent of blockchain has generated great interest as an alternative to centralized organizations, where the data is gathered through a centralized ledger keeping of activities of the firm. The decentralized ledger keeping is one of the main features of blockchain that has given rise to many issues of technology, development, implementation, privacy, acceptance, evaluation and so on. Blockchain concept is a follow-up to big data environment facilitated by enormous progress in computer hardware, storage capacities and technological prowess. This has resulted in the rapid acquiring of data not considered possible earlier. With shrewd modeling analytics and algorithms, the applications have grown to significant levels. This handbook discusses the progress in data collection, pros and cons of collecting information on decentralized publicly available ledgers and several applications.
Researchers in the social sciences and beyond are dealing more and more with massive quantities of text data requiring analysis, from historical letters to the constant stream of content in social media. Traditional texts on statistical analysis have focused on numbers, but this book will provide a practical introduction to the quantitative analysis of textual data. Using up-to-date R methods, this book will take readers through the text analysis process, from text mining and pre-processing the text to final analysis. It includes two major case studies using historical and more contemporary text data to demonstrate the practical applications of these methods. Currently, there is no introductory how-to book on textual data analysis with R that is up-to-date and applicable across the social sciences. Code and a variety of additional resources to enrich the use of this book are available on an accompanying website. These resources include data files from the 39th Congress, and also the collection of tweets of President Trump, now no longer available to researchers via Twitter itself.
Researchers in the social sciences and beyond are dealing more and more with massive quantities of text data requiring analysis, from historical letters to the constant stream of content in social media. Traditional texts on statistical analysis have focused on numbers, but this book will provide a practical introduction to the quantitative analysis of textual data. Using up-to-date R methods, this book will take readers through the text analysis process, from text mining and pre-processing the text to final analysis. It includes two major case studies using historical and more contemporary text data to demonstrate the practical applications of these methods. Currently, there is no introductory how-to book on textual data analysis with R that is up-to-date and applicable across the social sciences. Code and a variety of additional resources to enrich the use of this book are available on an accompanying website. These resources include data files from the 39th Congress, and also the collection of tweets of President Trump, now no longer available to researchers via Twitter itself.
Data mining is a mature technology. The prediction problem, looking for predictive patterns in data, has been widely studied. Strong me- ods are available to the practitioner. These methods process structured numerical information, where uniform measurements are taken over a sample of data. Text is often described as unstructured information. So, it would seem, text and numerical data are different, requiring different methods. Or are they? In our view, a prediction problem can be solved by the same methods, whether the data are structured - merical measurements or unstructured text. Text and documents can be transformed into measured values, such as the presence or absence of words, and the same methods that have proven successful for pred- tive data mining can be applied to text. Yet, there are key differences. Evaluation techniques must be adapted to the chronological order of publication and to alternative measures of error. Because the data are documents, more specialized analytical methods may be preferred for text. Moreover, the methods must be modi?ed to accommodate very high dimensions: tens of thousands of words and documents. Still, the central themes are similar.
In What Writing Does and How It Does It, editors Charles Bazerman and Paul Prior offer a sophisticated introduction to methods for understanding, studying, and analyzing texts and writing practices. This volume addresses a variety of approaches to analyzing texts, and considers the processes of writing, exploring textual practices and their contexts, and examining what texts do and how texts mean rather than what they mean. Included are traditional modes of analysis (rhetorical, literary, linguistic), as well as newer modes, such as text and talk, genre and activity analysis, and intertextual analysis. The chapters have been developed to provide answers to a specified set of questions, with each one offering: *a preview of the chapter's content and purpose; *an introduction to basic concepts, referring to key theoretical and research studies in the area; *details on the types of data and questions for which the analysis is best used; *examples from a wide-ranging group of texts, including educational materials, student writing, published literature, and online and electronic media; *one or more applied analyses, with a clear statement of procedures for analysis and illustrations of a particular sample of data; and *a brief summary, suggestions for additional readings, and a set of activities. The side-by-side comparison of methods allows the reader to see the multi-dimensionality of writing, facilitating selection of the best method for a particular research question. The volume contributors are experts from linguistics, communication studies, rhetoric, literary analysis, document design, sociolinguistics, education, ethnography, and cultural psychology, and each utilizes a specific mode of text analysis. With its broad range of methodological examples, What Writing Does and How It Does It is a unique and invaluable resource for advanced undergraduate and graduate students and for researchers in education, composition, ESL and applied linguistics, communication, L1 and L2 learning, print media, and electronic media. It will also be useful in all social sciences and humanities that place importance on texts and textual practices, such as English, writing, and rhetoric.
A guide for using computational text analysis to learn about the social world From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights. Text as Data is organized around the core tasks in research projects using text—representation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research. Bridging many divides—computer science and social science, the qualitative and the quantitative, and industry and academia—Text as Data is an ideal resource for anyone wanting to analyze large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain. Overview of how to use text as data Research design for a world of data deluge Examples from across the social sciences and industry
Chapter 7. Case Study : Comparing Twitter Archives; Getting the Data and Distribution of Tweets; Word Frequencies; Comparing Word Usage; Changes in Word Use; Favorites and Retweets; Summary; Chapter 8. Case Study : Mining NASA Metadata; How Data Is Organized at NASA; Wrangling and Tidying the Data; Some Initial Simple Exploration; Word Co-ocurrences and Correlations; Networks of Description and Title Words; Networks of Keywords; Calculating tf-idf for the Description Fields; What Is tf-idf for the Description Field Words?; Connecting Description Fields to Keywords; Topic Modeling.
The SAGE Handbook of Research Methods in Political Science and International Relations offers a comprehensive overview of research processes in social science — from the ideation and design of research projects, through the construction of theoretical arguments, to conceptualization, measurement, & data collection, and quantitative & qualitative empirical analysis — exposited through 65 major new contributions from leading international methodologists. Each chapter surveys, builds upon, and extends the modern state of the art in its area. Following through its six-part organization, undergraduate and graduate students, researchers and practicing academics will be guided through the design, methods, and analysis of issues in Political Science and International Relations: Part One: Formulating Good Research Questions & Designing Good Research Projects Part Two: Methods of Theoretical Argumentation Part Three: Conceptualization & Measurement Part Four: Large-Scale Data Collection & Representation Methods Part Five: Quantitative-Empirical Methods Part Six: Qualitative & "Mixed" Methods
A unique anthology of textual analysis methodologies, this book offers a thorough introduction to the key approaches and the tools students need to implement them. Every chapter contains not just the theory behind each methodology, but also its advantages and disadvantages, its problems with ontology and language, and its relationship to studying social phenomenon. Through contemporary and relatable real-world worked examples, the book illustrates different contexts in which a methodology has been successfully used and allows students to see the methods in action and extrapolate the techniques into their own research. Methods included: Content analysis Argumentation analysis Qualitative analysis of ideas Narrative analysis Metaphor analysis Multimodal discourse analysis Discourse analysis Engaging and authoritative in equal measure, this guide to textual analysis is the perfect foundation for students conducting research in the social sciences.