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Data Science and Social Research

Epistemology, Methods, Technology and Applications

  • Conference proceedings
  • © 2017


  • 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:

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About this book

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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Table of contents (28 papers)

  1. Methods, Software and Data Architectures

  2. On-Line Data Applications

Editors and Affiliations

  • Department of Economy and Statistics, University of Naples Federico II, Naples, Italy

    N. Carlo Lauro

  • Department of Social Sciences, University of Naples Federico II, Naples, Italy

    Enrica Amaturo, Maria Gabriella Grassia, Biagio Aragona, Marina Marino

About the editors

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.

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