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Gregor Wiedemann evaluates text mining applications for social science studies with respect to conceptual integration of consciously selected methods, systematic optimization of algorithms and workflows, and methodological reflections relating to empirical research. In an exemplary study, he introduces workflows to analyze a corpus of around 600,000 newspaper articles on the subject of “democratic demarcation” in Germany. He provides a valuable resource for innovative measures to social scientists and computer scientists in the field of applied natural language processing.
This book provides an overview of a range of quantitative methods, presenting a thorough analytical toolbox which will be of practical use to researchers across the social sciences as they face the challenges raised by new technology-driven language practices. The book is driven by a reflexive mind-set which views quantifying methods as complementary rather than in opposition to qualitative methods, and the chapters analyse a multitude of different intra- and extra-textual context levels essential for the understanding of how meaning is (re-)constructed in society. Uniting contributions from a range of national and disciplinary traditions, the chapters in this volume bring together state-of-the-art research from British, Canadian, French, German and Swiss authors representing the fields of Political Science, Sociology, Linguistics, Computer Science and Statistics. It will be of particular interest to discourse analysts, but also to other scholars working in the digital humanities and with big data of any kind.
The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This second volume focuses on foundations and advances in data science, statistical modeling, and machine learning. It covers a range of key issues, including the management of big data in terms of record linkage, streaming, and missing data. Machine lea...
How are human computation systems developed in the field of citizen science to achieve what neither humans nor computers can do alone? Through multiple perspectives and methods, Libuse Hannah Veprek examines the imagination of these assemblages, their creation, and everyday negotiation in the interplay of various actors and play/science entanglements at the edge of AI. Focusing on their human-technology relations, this ethnographic study shows how these formations are marked by intraversions, as they change with technological advancements and the actors' goals, motivations, and practices. This work contributes to the constructive and critical ethnographic engagement with human-AI assemblages in the making.
Probabilistic topic models have proven to be an extremely versatile class of mixed-membership models for discovering the thematic structure of text collections. There are many possible applications, covering a broad range of areas of study: technology, natural science, social science and the humanities. In this thesis, a new efficient parallel Markov Chain Monte Carlo inference algorithm is proposed for Bayesian inference in large topic models. The proposed methods scale well with the corpus size and can be used for other probabilistic topic models and other natural language processing applications. The proposed methods are fast, efficient, scalable, and will converge to the true posterior d...
The two-volume set LNCS 11185 + 11186 constitutes the proceedings of the 10th International Conference on Social Informatics, SocInfo 2018, held in Saint-Petersburg, Russia, in September 2018. The 30 full and 32 short papers presented in these proceedings were carefully reviewed and selected from 110 submissions. They deal with the applications of methods of the social sciences in the study of socio-technical systems, and computer science methods to analyze complex social processes, as well as those that make use of social concepts in the design of information systems.
The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This first volume focuses on the scope of computational social science, ethics, and case studies. It covers a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introd...
This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read at Oxford Scholarship Online and offered as a free PDF download from OUP and selected open access locations.Deliberative democracy is a diverse and rapidly growing field of research. But how can deliberative democracy be studied? Research Methods in Deliberative Democracy provides a unique collection of over 30 methods to study deliberative democracy. Written in an accessible style, it provides guidancefor scholars and students on how to conduct rigorous and creative research on the public sphere, structured forums, and political institutions. Each chapter introduces a particular method, elaborates its utility in deliberative democracy research, and provides guidance on its application, as well asillustrations from previous studies. This book celebrates the methodological pluralism in the field, and hopes to inspire scholars to undertake methodologically robust, intellectually creative, and politically relevant empirical research.