Data Science and Social Research

Epistemology, Methods, Technology and Applications

  • N. Carlo Lauro
  • Enrica Amaturo
  • Maria Gabriella Grassia
  • Biagio Aragona
  • Marina Marino
Conference proceedings

Table of contents

  1. Front Matter
    Pages i-ix
  2. Enrica Amaturo, Biagio Aragona
    Pages 1-6
  3. Epistemology

  4. Methods, Software and Data Architectures

    1. Front Matter
      Pages 79-79
    2. Emma Zavarrone, Filomena Grassia, Maria Gabriella Grassia, Marina Marino
      Pages 105-119
    3. Laura Antonucci, Marina Basile, Corrado Crocetta, Viviana D’Addosio, Francesco D. d’Ovidio, Domenico Viola
      Pages 121-129
    4. Andrea Amico, Giampiero D’Alessandro, Alessandra Decataldo
      Pages 131-139
    5. Maria Carmela Catone, Paolo Diana, Marisa Faggini
      Pages 151-161
  5. On-Line Data Applications

    1. Front Matter
      Pages 163-163
    2. Giovanni Giuffrida, Simona Gozzo, Francesco Mazzeo Rinaldi, Venera Tomaselli
      Pages 165-174
    3. Fabrizio Esposito, Estella Esposito, Pierpaolo Basile
      Pages 175-183
    4. Rosanna Cataldo, Roberto Galasso, Maria Gabriella Grassia, Marino Marina
      Pages 185-192
    5. Maka Eradze, Kairit Tammets
      Pages 193-204
    6. Grazia Biorci, Antonella Emina, Michelangelo Puliga, Lisa Sella, Gianna Vivaldo
      Pages 205-213
    7. Antonio Ruoto, Vito Santarcangelo, Davide Liga, Giuseppe Oddo, Massimiliano Giacalone, Eugenio Iorio
      Pages 215-222
    8. D. Borrelli, R. Serpieri, D. Taglietti, D. Trezza
      Pages 223-236
  6. Off-Line Data Applications

  7. Maria Carmela Catone, Paolo Diana, Marisa Faggini
    Pages E1-E1

About these proceedings


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.


data analysis in social sciences data science and social sciences data science new data textual analytics social network analysis epistemology of social sciences on-line data analysis off-line data analysis software and data architectures big data

Editors and affiliations

  • N. Carlo Lauro
    • 1
  • Enrica Amaturo
    • 2
  • Maria Gabriella Grassia
    • 3
  • Biagio Aragona
    • 4
  • Marina Marino
    • 5
  1. 1.Department of Economy and StatisticsUniversity of Naples Federico IINaplesItaly
  2. 2.Department of Social SciencesUniversity of Naples Federico IINaplesItaly
  3. 3.Department of Social SciencesUniversity of Naples Federico IINaplesItaly
  4. 4.Department of Social SciencesUniversity of Naples Federico IINaplesItaly
  5. 5.Department of Social SciencesUniversity of Naples Federico IINaplesItaly

Bibliographic information