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Social phenomena: from data analysis to models

Social phenomena: from data analysis to models

Gonçalves, Bruno and Perra, Nicola, (eds.) (2015) Social phenomena: from data analysis to models. Computational Social Sciences . Springer International Publishing, Cham, Switzerland. ISBN 978-3-319-14010-0

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Abstract

This book focuses on the new possibilities and approaches to social modeling currently being made possible by an unprecedented variety of datasets generated by our interactions with modern technologies. This area has witnessed a veritable explosion of activity over the last few years, yielding many interesting and useful results. Our aim is to provide an overview of the state of the art in this area of research, merging an extremely heterogeneous array of datasets and models. Social Phenomena: From Data Analysis to Models is divided into two parts. Part I deals with modeling social behavior under normal conditions: How we live, travel, collaborate and interact with each other in our daily lives. Part II deals with societal behavior under exceptional conditions: Protests, armed insurgencies, terrorist attacks, and reactions to infectious diseases. This book offers an overview of one of the most fertile emerging fields bringing together practitioners from scientific communities as diverse as social sciences, physics and computer science. We hope to not only provide an unifying framework to understand and characterize social phenomena, but also to help foster the dialogue between researchers working on similar problems from different fields and perspectives.

Item Type: Book
Additional Information: © Springer International Publishing Switzerland
Uncontrolled Keywords: Social phenomena, Computational social science
Faculty / Department / Research Group: Faculty of Business > Centre for Business Network Analysis (CBNA)
Faculty of Business > Networks and Urban Systems Centre (NUSC) > Centre for Business Network Analysis (CBNA)
Last Modified: 21 Dec 2017 12:23
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/13900

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