Data Science and Social Research : Epistemology, Methods, Technology and Applications. 1st ed. 2017

種類:

電子ブック

責任表示:

edited by N. Carlo Lauro, Enrica Amaturo, Maria Gabriella Grassia, Biagio Aragona, Marina Marino

出版情報:

Cham : Springer International Publishing : Imprint: Springer, 2017

著者名:

Lauro, N. Carlo.

Amaturo, Enrica.

Grassia, Maria Gabriella.

Aragona, Biagio.

Marino, Marina.

SpringerLink (Online service)

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シリーズ名:

Studies in Classification, Data Analysis, and Knowledge Organization ;

ISBN:

9783319554778 [3319554778]    

注記:

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
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

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