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  • Cathleen M. Stuetzer; Martin Welker; Marc Egger: Big Data Analytics : Obstacles and Opportunities for Social Science (9-14). I. EPISTEMOLOGICAL PERSPECTIVES: Brenda L. Berkelaar; Luis Francisco -Revilla: Motivation, Evidence, and Computation: A Research Framework for Expanding Computational Social Science Participation and Design (16-62); Biagia Aragona: Beyond Data Driven Social Science: Researching Big Data Assemblages (63-76); Jan R. Riebling: The Medium Data Problem in Social Science (77-101). II. DATA, METHODS, AND INSTRUMENTS: Jakob Junger: Mapping the Field of Automated Data Collection on the Web: Collection Approaches, Data Types, and Research Logic; Mareike Wieland; Anne-Marie in der Au; Christine Keller; Sören Brunk; Thomas Bettermann; Lutz M. Hagen; Thomas Schlegel: Online Behavior Tracking in Social Sciences : Quality Criteria and Technical Implementation (131-160); Elisabeth Günther; Damian Trilling; Bob van de Velde: But How Do We Store It? : (Big) Data Architecture in the Social-Scientific Research Process (161-187); Fionn Murtagh: The Geometric Data Analysis and Correspondence Analysis Platform : New Potential and New Challenges, Including Ethics, of Big Data Analytics; Ulrike Brandes; Michael Hamann; Mark Ortmann; Dorothea Wagner: On the Persistence of Strongly Embedded Ties (213-234); Yannis Skarpelos: Big Visual Data in Social Sciences (235-265). III. CASESTUDIES: Ji-Ping Lin: Human Relationships and Kinship Analytics from Big Data Based on Data Science : A Study on Ethnic Marriage and Identity Using Taiwan's Indigenous Peoples as an Example; Alessandro Cimbelli; Cinzia Conti; Fiorenza Deriu: The Use of Big Data in Studying Migration Routes : New Tools and Applications (303-325); Theoni Stathopoulou; Haris Papageorggiou; Konstantina Papanikolaou; Athanasia Kolovou: Exploring the Dynamics of Protest with Automated Computational Tools: A Greek Case Study (326-354); Jérémy Ducros; Elisa Grandi; Pierre-Cyrille Hautcoeur; Raphael Hekimian; Emmanuel Prunaux; Angelo Riva; Stefano Ungaro: Collecting and Storing Historical Financial Data: DFIH Project (355-377); Daniel Richter; Michael Bartl: Affective Computing Applied to a Recipe Recommendation System (378-394). IV.TUTORIAL SECTION: Rianne Conijn; Wouter Nij Bijvank; Chris Snijders; Ad Kleingeld; Uwe Matzat: From Raw to Ready-made Data: A Hands-on Manual for Pre-processing Learning Management System Log Data for Learning Analytics (396-422); Yannick Rieder; Simon Kühne: Geospatial Analysis of Social Media Data - A Practical Framework and Applications Using Twitter (423-446). (xsd:string)
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  • Computational social science in the age of big data : concepts, methodologies, tools, and applications (xsd:string)
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  • Köln: von Halem, 2018.- 457 S., Abb.; Kt. (xsd:string)
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