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Data Science and Social Research: Epistemology, Methods, Technology and Applications de N. Carlo Lauro

Data Science and Social Research: Epistemology, Methods, Technology and Applications: Studies in Classification, Data Analysis, and Knowledge Organization

Editat de N. Carlo Lauro, Enrica Amaturo, Maria Gabriella Grassia, Biagio Aragona, Marina Marino
en Limba Engleză Paperback – 18 noi 2017
This edited volume lays the groundwork for Social Data Science, addressing epistemological issues, methods, technologies, software and applications of data science in the social sciences. It presents data science techniques for the collection, analysis and use of both online and offline new (big) data in social research and related applications. Among others, the individual contributions cover topics like social media, learning analytics, clustering, statistical literacy, recurrence analysis and network analysis.
Data science is a multidisciplinary approach based mainly on the methods of statistics and computer science, and its aim is to develop appropriate methodologies for forecasting and decision-making in response to an increasingly complex reality often characterized by large amounts of data (big data) of various types (numeric, ordinal and nominal variables, symbolic data, texts, images, data streams, multi-way data, social networks etc.) and from diverse sources.
This book presents selected papers from the international conference on Data Science & Social Research, held in Naples, Italy in February 2016, and will appeal to researchers in the social sciences working in academia as well as in statistical institutes and offices.
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Specificații

ISBN-13: 9783319554761
ISBN-10: 331955476X
Pagini: 350
Ilustrații: IX, 300 p. 77 illus., 51 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.44 kg
Ediția:1st ed. 2017
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Classification, Data Analysis, and Knowledge Organization

Locul publicării:Cham, Switzerland

Cuprins

Preface.- INDEX.- Introduction. Enrica Amaturo, Biagio Aragona.- Part I Epistemology: On Data, Big Data and Social Research. Is It a Real Revolution? Federico Neresini.-New Data Science - The Sociological Point of View; Biagio Aragona.- Data Revolutions in Sociology; Barbara Saracino.- Blurry Boundaries: Internet, Big New Data and Mixed-Method Approach; Enrica Amaturo, Gabriella Punziano.- Social Media and the Challenge of Big Data/Deep Data Approach; Giovanni Boccia Artieri.- Governing by Data - Some Considerations on the Role of Learning Analytics in Education; Rosanna De Rosa.- Part II Methods, Software and Data Architectures: A Knowledge-based Model for Clustering and Hierarchical Disjoint Non-negative Factor Analysis; Mario Fordellone, Maurizio Vichi.- TaLTaC 3.0. A Multi-levelWeb Platform for Textual Big Data in the
Social Sciences; Sergio Bolasco and Giovanni De Gasperis.- Latent Growth and Statistical Literacy; Emma Zavarrone.- University of Bari’s Website Evaluation; Laura Antonucci, Marina Basile, Corrado Crocetta, Viviana D’Addosio, Francesco D. d’Ovidio, Domenico Viola.- Advantages of Administrative Data - Three Analyses of Students’ Careers in Higher Education; Andrea Amico, Giampiero D’Alessandro, Alessandra Decataldo.- Growth Curve Models to Detect Walking Impairment: the Case of InCHIANTI Study; Catia Monicolini, Carla Rampichini.-  1)       Recurrence Analysis - Method and Applications; Maria Carmela Catone, Paolo Diana, Marisa Faggini.- Part III On-line Data Applications: Big Data and Network Analysis - A Promising Integration for Decision-Making; Giovanni Giuffrida, Simona Gozzo, Francesco Mazzeo Rinaldi, Venera Tomaselli.- White House Under Attack - Introducing Distributional Semantic Model for the Analysis of US Crisis Communication Strategies; Fabrizio Esposito, Estella Esposito, Pierpaolo Basile.- #theterrormood - Studying the World Mood after the Terror Attacks on Paris and Bruxelles; Rosanna Cataldo, Roberto Galasso, Maria Gabriella Grassia, Marina Marino; Learning Analytics in MOOCs - EMMA Case; Maka Eradze, and Kairit Tammets.- Tweet-Tales: Moods of Socio-Economic Crisis? Grazia Biorci, Antonella Emina, Michelangelo Puliga, Lisa Sella, Gianna Vivaldo.- The Sentiment of the Infosphere - A Sentiment Analysis Approach for the Big Conversation on the Net; Antonio Ruoto, Vito Santarcangelo, Davide Liga, Giuseppe Oddo, Massimiliano Giacalone, Eugenio Iorio.- The Promises of Sociological Degrees - A Lexi-cal Correspondence Analysis of Masters Syllabi; Davide Borrelli, Roberto Serpieri, Danilo Taglietti, Domenico Trezza.- Part IV Off-line Data Applications: Exploring Barriers in the Sustainable Microgeneration Preliminary In-sights Thought the PLS-PM Approach; Ivano Scotti, Dario Minervini.- Individual Disadvantage and Training Policies - TheConstructions of "Model-based" Composite Indicators; Rosanna Cataldo - Maria Gabriella Grassia - Carlo Lauro - Elena Ragazzi - Lisa Sella.- Measuring the Intangibles - Testing the Human Capital Theory Against the OECD Programme for the International Assessment of Adult Competencies; Federica Cornali.- Analysis of the Employment Transitions and Analysis of the Unemployment Risk in the Social Security Account Statements of the Patronato ACLI; Danilo Catania, Alessandro Serini, Gianfranco Zucca.- Integrated Education Microdata to Support Statistics Production; Maria Carla Runci, Grazia Di Bella and Francesca Cuppone.- Latent Growth and Statistical Literacy; Zavarrone, Grassia.

Notă biografică

Carlo Natale Lauro is Professor Emeritus of Statistics at the University of Naples Federico II, where he was Chair of the Ph.D. course on computational statistics (1988-2014). He was President of the International Association for Statistical Computing and International Federation of Classification Societies. His main scientific interests include data science, multivariate analysis, computational statistics and data mining.
Enrica Amaturo is Full Professor of Sociology and Head of the Department of Social Sciences of the University of Naples Federico II. She is President of the Italian Sociological Association and was a member of the Italian Commission on Social Exclusion (1999-2001; 2007-2011). Her main interests are methods for the analysis of new media, mixed-methods research and the analysis of social exclusion.
Biagio Aragona is Assistant Professor of Sociology at the Department of Social Sciences of the University of Naples Federi
co II, where he teaches social research methods and advanced methods for quantitative research. His research activities primarily involve the use of statistical sources for the analysis of social inequalities and the analysis of the challenges and opportunities that new data offer for the social sciences.Maria Gabriella Grassia is Associate Professor of Social Statistics at the Department of Social Sciences of the University of Naples Federico II, where she also serves on the research committee for the Ph.D. program on social science and statistics. From 2008 to 2012, she was a Council Officer of the Italian Statistical Society. Her research areas include multivariate analysis, text mining and composite indicators.
Marina Marino is Associate Professor of Statistics at the Department of Social Sciences of the University of Naples Federico II, where she is also a member of the research committee for the Ph.D. program on social Sci
ence and statistics. Her chief research areas are computational statistics, data mining, classification and clustering, statistical analysis of interval-valued data and composite indicators.

Textul de pe ultima copertă

This edited volume lays the groundwork for Social Data Science, addressing epistemological issues, methods, technologies, software and applications of data science in the social sciences. It presents data science techniques for the collection, analysis and use of both online and offline new (big) data in social research and related applications. Among others, the individual contributions cover topics like social media, learning analytics, clustering, statistical literacy, recurrence analysis and network analysis.
Data science is a multidisciplinary approach based mainly on the methods of statistics and computer science, and its aim is to develop appropriate methodologies for forecasting and decision-making in response to an increasingly complex reality often characterized by large amounts of data (big data) of various types (numeric, ordinal and nominal variables, symbolic data, texts, images, data streams, multi-way data, social networks etc.) and from diverse sources.
This book presents selected papers from the international conference on Data Science & Social Research, held in Naples, Italy in February 2016, and will appeal to researchers in the social sciences working in academia as well as in statistical institutes and offices.

Caracteristici

Applies methods and techniques of data science to the social sciences Provides extensive examples of new (big) data use in the social sciences Discusses epistemological consequences of new data on social sciences Features a section on on-line data analysis Includes supplementary material: sn.pub/extras