Handbook of Computational Social Science Volume 2

<p>The<em> Handbook of Computational Social Science </em>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.</p><p>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 learning agent-based and statistical modeling as well as data quality in relation to digital trace and textual data as well as probability non-probability and crowdsourced samples represent further foci. The volume not only makes major contributions to the consolidation of this growing research field but also encourages growth into new directions.</p><p>With its broad coverage of perspectives (theoretical methodological computational) international scope and interdisciplinary approach this important resource is integral reading for advanced undergraduates postgraduates and researchers engaging with computational methods across the social sciences as well as those within the scientific and engineering sectors.</p>