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. The
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, introduces
digital trace data, reviews the changing survey landscape, and
presents novel examples of computational social science research on
sensing social interaction, social robots, bots, sentiment,
manipulation, and extremism in social media. The volume not only
makes major contributions to the consolidation of this growing
research field, but also encourages growth into new directions. The
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. 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.
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