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The increased attention to (big) data also affects researching social and cultural phenomena. Novel methods, innovative tools and new resources for data present researchers with both opportunities and challenges. This book maps practices and methods for data analysis and visualization. It critically reflects on the role of data and knowledge technologies in academia and society.
List of contents
Acknowledgements, Foreword, Introduction: New Brave World Karin van Es and Mirko Tobias Schäfer, Section 1: Studying Culture through Data Humanistic Data Research: An Encounter between Epistemic Traditions Eef Masson Towards a 'Humanistic Cinemetrics'? Christian Gosvig Olesen Cultural Analytics, Social Computing, and Digital Humanities Lev Manovich Case Study: On Broadway Daniel Goddemeyer, Moritz Stefaner, Dominikus Baur, and Lev Manovich Foundations of Digital Methods: Query Design Richard Rogers Case Study: Webs and Streams: Mapping Issue-Networks Using Hyperlinks, Hashtags, and (Potentially) Embedded Content Natalia Sánchez-Querubín, Section 2: Data Practices in Digital Data Analysis Digital Methods: From Challenges to Bildung Bernhard Rieder and Theo Röhle Data, Culture, and the Ambivalence of Algorithms William Uricchio Unknowing Algorithms: On Transparency of Un-operable Black Boxes Johannes Paßmann and Asher Boersma Social Data APIs: Origin, Types, Issues Cornelius Puschmann and Julian Ausserhofer How to Tell Stories with Networks: Exploring the Narrative Affordances of Graphs with the Iliad Tommaso Venturini, Liliana Bounegru, Mathieu Jacomy, and Jonathan Gray Towards a Reflexive Digital Data Analysis Karin van Es, Nicolás López Coombs and Thomas Boeschoten, Section 3: Research Ethics Get Your Hands Dirty: Research Ethics in an Age of Big Data: How Digital Methods and 'Big Data' Practices Challenge Traditional Guidelines for Research Integrity Gerwin van Schie, Irene Westra, and Mirko Tobias Schäfer Research Ethics in Context: Decision-Making in Digital Research Annette Markham and Elizabeth Buchanan Data and Discrimination Koen Leurs and Tamara Shepherd ,Section 4: Key Ideas in Big Data Research: The Myth of Big Data, Data Point Critique, Algorithmic Exceptionalism of Algorithms and the Need for a Dialogue with Technology The Myth of Big Data Nick Couldry Data-Point Critique Carolin Gerlitz Opposing the Exceptionalism of the Algorithm Evgeny Morozov The Need for a Dialogue with Technology Mercedes Bunz Tools, Notes on Contributors, Index.
About the author
Mirko Tobias Schäfer is Associate Professor of AI, Data & Society at Utrecht University's research area 'Governing the Digital Society' and the Department for Information and Computing Sciences. Mirko is co-founder and Sciences lead of the Data School. He studies the datafication of public management and engages in the development of responsible and accountable AI and data practices. Karin van Es is Associate Professor of Media and Culture Studies and project lead Humanities at Data School, both at Utrecht University.
