This open access handbook describes foundational issues methodological approaches and examples
on how to analyse and model data using Computational Social Science (CSS) for policy support.
Up to now CSS studies have mostly developed on a small proof-of concept scale that prevented
from unleashing its potential to provide systematic impact to the policy cycle as well as from
improving the understanding of societal problems to the definition assessment evaluation and
monitoring of policies. The aim of this handbook is to fill this gap by exploring ways to
analyse and model data for policy support and to advocate the adoption of CSS solutions for
policy by raising awareness of existing implementations of CSS in policy-relevant fields. To
this end the book explores applications of computational methods and approaches like big data
machine learning statistical learning sentiment analysis text mining systems modelling and
network analysis to different problems in the social sciences. The book is structured into
three Parts: the first chapters on foundational issues open with an exposition and description
of key policymaking areas where CSS can provide insights and information. In detail the
chapters cover public policy governance data justice and other ethical issues. Part two
consists of chapters on methodological aspects dealing with issues such as the modelling of
complexity natural language processing validity and lack of data and innovation in official
statistics. Finally Part three describes the application of computational methods challenges
and opportunities in various social science areas including economics sociology demography
migration climate change epidemiology geography and disaster management. The target
audience of the book spans from the scientific community engaged in CSS research to
policymakers interested in evidence-informed policy interventions but also includes private
companies holding data that can be used to study social sciences and are interested in
achieving a policy impact.
