Handbook of Computational Social Science for Policy

Preface
	References
Chapter Correspondence Between Mapping the Demand Side of Computational Social Science for Policy andHandbook of Computational Social Science for Policy
Acknowledgments
Contents
Abbreviations
Part I Foundational Issues
	1 Computational Social Science for Public Policy
		1.1 Introduction
		1.2 Detection
		1.3 Measurement
		1.4 Prediction
		1.5 Etiology
		1.6 Simulation
		1.7 An Ethics-Driven Computational Social Science
		1.8 Building Resilience: CSS at the Heart of a Reinvented Policy Toolkit
		1.9 Conclusion
		References
	2 Computational Social Science for the Public Good: Towards a Taxonomy of Governance and Policy Challenges
		2.1 Introduction
		2.2 Data Ecosystem Challenges
			2.2.1 Data Accessibility: Paucity and Asymmetries
			2.2.2 Misaligned or Negative Incentives for Collaborating
			2.2.3 Poorly Understood (and Studied) Value Proposition, Benefits, and Risks
		2.3 Data Governance Challenges
			2.3.1 Data Reuse, Purpose Specification, and Minimization
			2.3.2 Data Anonymization and Re-identification
			2.3.3 Data Rights (Co-generated Data) and Sovereignty
			2.3.4 Barriers to Data Portability, Interoperability, and Platform Portability
			2.3.5 Data Ownership and Licensing
		2.4 Research Design Challenges
			2.4.1 Injustice and Bias in Data and Algorithms
			2.4.2 Data Accuracy and Quality
			2.4.3 Data Invisibles and Systemic Inequalities
		2.5 Computational Structures and Processes Challenges
			2.5.1 Human Computation, Collective Intelligence, and Exploitation
			2.5.2 Need for Increased Computational Processing Power and Tackling Related Environmental Challenges
		2.6 Scientific Ecosystem Challenges
			2.6.1 Domain, Computational, and Data Expertise: The Need for Interdisciplinary Collaboration Networks
			2.6.2 Conflict of Interests, Corporate Funding, Data Donation Dependencies, and Other Ethical Limitations
			2.6.3 The Failure of Reproducibility
		2.7 Societal Impact Challenges
			2.7.1 Need for Citizen/Community Engagement and Acquiring a Social License
			2.7.2 Lack of Data Literacy and Agency
			2.7.3 Computational Solutionism and Determinism
			2.7.4 Computational/Data Surveillance and the Risk of Exploitation
		2.8 Reflections and Conclusion
		References
	3 Data Justice, Computational Social Science and Policy
		3.1 Introduction
		3.2 Background: Computational Social Science and Data Justice
		3.3 Questions and Challenges
			3.3.1 Who Benefits?
			3.3.2 Making People Visible: Surveillance as Social Science
		3.4 Addressing Justice Concerns: Ethics, Regulation and Governance
		3.5 The Way Forward
		References
	4 The Ethics of Computational Social Science
		4.1 Introduction
		4.2 Ethical Challenges Faced by CSS
			4.2.1 Challenges Related to the Treatment of Research Subjects
			4.2.2 Challenges Related to the Impacts of CSS Research on Affected Individuals and Communities
				4.2.2.1 Adverse Impacts at the Individual Level
				4.2.2.2 Adverse Impacts at the Social Level
				4.2.2.3 Adverse Impacts at the Biospheric Level
			4.2.3 Challenges Related to the Quality of CSS Research and to Its Epistemological Status
			4.2.4 Challenges Related to Research Integrity
			4.2.5 Challenges Related to Research Equity
		4.3 Incorporating Habits of Responsible Research and Innovation into CSS Practices
			4.3.1 Consider Context
			4.3.2 Anticipate Impacts
			4.3.3 Reflect on Purposes, Positionality, and Power
			4.3.4 Engage Inclusively
			4.3.5 Act Transparently and Responsibly
		4.4 Conclusion
		References
Part II Methodological Aspects
	5 Modelling Complexity with Unconventional Data: Foundational Issues in Computational Social Science
		5.1 Introduction
		5.2 Existing Literature
		5.3 Addressing Foundational Issues of CSS for Policy
			5.3.1 Data as the Input of the Process
			5.3.2 Modelling Techniques for New Data
			5.3.3 Policy Recommendation as an Output of the Process
		5.4 The Way Forward
		References
	6 From Lack of Data to Data Unlocking
		6.1 Introduction: Data for Causal Policy Analysis
			6.1.1 The Variety of Data
			6.1.2 Underlying Statistical Issue: The Culture of Open Access
		6.2 Computational Statistical Issues
			6.2.1 Statistical Issues with Merging Big Data
			6.2.2 The Statistical Issue of Replicability and Data Security
			6.2.3 Statistical Issues Risen by Anonymity Concerns and Related Challenges
		6.3 The Way Forward
		6.4 Conclusion
		References
	7 Natural Language Processing for Policymaking
		7.1 Introduction
		7.2 NLP for Text Analysis
			7.2.1 Text Classification
			7.2.2 Topic Modelling
			7.2.3 Event Extraction
			7.2.4 Score Prediction
		7.3 Using NLP for Policymaking
			7.3.1 Analysing Data for Evidence-Based Policymaking
			7.3.2 Interpreting Political Decisions
			7.3.3 Improving Policy Communication with the Public
			7.3.4 Investigating Policy Effects
		7.4 Limitations and Ethical Considerations
		7.5 Conclusions
		References
	8 Describing Human Behaviour Through Computational Social Science
		8.1 Introduction
		8.2 Data in the Digital World
		8.3 Behavioural Digital Data
		8.4 Online Population-Based Survey Experiments
		8.5 Heterogeneity Analysis and Computational Methods
		8.6 Conclusions
		References
	9 Data and Modelling for the Territorial Impact Assessment (TIA) of Policies
		9.1 Introduction
		9.2 TIA: A Literature Review
		9.3 Computational Guidelines on TIA
			9.3.1 The Main Contribution of Computational Social Science for Territorial Impact Assessment
				9.3.1.1 Complementarity
				9.3.1.2 Real-Time Information
				9.3.1.3 Spatial Accuracy
			9.3.2 Sources of Data Towards an Analysis of EU Territorial Heterogeneity
			9.3.3 Main Challenges on Using Non-traditional Sources of Data on Implementing TIA Methodologies
				9.3.3.1 The Relevance of the Sample
				9.3.3.2 Precise Location and Low Cost of Collected Data
				9.3.3.3 Easy Access and Real-Time Data
		9.4 The Way Forward
		References
	10 Challenges and Opportunities of Computational Social Science for Official Statistics
		10.1 Introduction
		10.2 Current Official Statistics Systems
			10.2.1 Statistical Principles
			10.2.2 Recognition of the Value of New Data Sources
			10.2.3 Some Proof of Concepts and Experiences
		10.3 The Need for Change
			10.3.1 Data Access
			10.3.2 Adapting the Official Statistics System
			10.3.3 Effective Use of the New Sources
		10.4 The Way Forward
		References
Part III Applications
	11 Agriculture, Food and Nutrition Security: Concept, Datasets and Opportunities for Computational Social Science Applications
		11.1 Introduction
		11.2 The Complex Pathways to Nutritional Outcomes: A Conceptual Note
		11.3 Current Ex Ante Analytical Models for Nutritional Policy Insights
		11.4 The Data Scarcity for Nutritional Modelling and Analytics
		11.5 Novel Digital Food and Nutrition Data for Computational Analytics
		11.6 The Way Forward
		References
	12 Big Data and Computational Social Science for Economic Analysis and Policy
		12.1 Introduction
		12.2 Big Data in Economics
			12.2.1 Administrative Data
			12.2.2 Financial Data
			12.2.3 Labour Markets
			12.2.4 Textual Data
			12.2.5 Mobile Phone Data
			12.2.6 Internet Data
			12.2.7 Other Data
		12.3 Conclusion
		References
	13 Changing Job Skills in a Changing World
		13.1 Introduction
		13.2 Existing Literature
		13.3 Computational Guidelines
		13.4 The Way Forward
		References
	14 Computational Climate Change: How Data Science and Numerical Models Can Help Build Good Climate Policies and Practices
		14.1 Introduction
		14.2 Modelling the Climate Economy
			14.2.1 Model Paradigms
			14.2.2 Modelling Relevance for Climate Policy
			14.2.3 Challenges in Using Integrated Assessment Models to Inform Societal Change
		14.3 Data Science for Climate Impacts and Policy
			14.3.1 Data-Driven Approaches for Climate Economics
			14.3.2 Relevance of Empirical Methods for Climate Policy
		14.4 Towards an Integrated Computational Approach
		References
	15 Digital Epidemiology
		15.1 Introduction
		15.2 Existing Literature
		15.3 Computational Guidelines
			15.3.1 Infectious Diseases
			15.3.2 Non-communicable Diseases
			15.3.3 Mental Illness and Suicide
			15.3.4 Beliefs, Information, and Misinformation
		15.4 The Way Forward
		References
	16 Learning Analytics in Education for the Twenty-First Century
		16.1 Introduction
		16.2 Potential for Educators and Citizens
			16.2.1 Growing Opportunities for Data-Driven Policies in Education
			16.2.2 Learning Analytics as a Toolset
				16.2.2.1 Improving Cost-Effectiveness of Education
				16.2.2.2 Improving Learning Outcomes
				16.2.2.3 Educational Quality Management
				16.2.2.4 Underlying Data for Quality Measurement
				16.2.2.5 Efficiency Measurement
				16.2.2.6 Predictions
		16.3 An Array of Policy-Driving Tools
			16.3.1 Bayesian Additive Regression Trees
			16.3.2 Social Network Analysis
			16.3.3 Natural Language Processing
		16.4 Issues and Recommendations
			16.4.1 Non-technical Issues
			16.4.2 Technical Issues
			16.4.3 Recommendations
		References
	17 Leveraging Digital and Computational Demography for Policy Insights
		17.1 Introduction
		17.2 The Digital Turn in Demography: An Overview
			17.2.1 Advances in Data Opportunities
			17.2.2 Computational Methods for Demographic Questions
			17.2.3 Demographic Impacts of Digitalization
		17.3 Computational Guidelines
			17.3.1 Methodological Opportunities
			17.3.2 Data Opportunities
			17.3.3 Understanding Demographic Heterogeneity in the Impacts of Digital Technologies
		17.4 Discussion
		References
	18 New Migration Data: Challenges and Opportunities
		18.1 Introduction
		18.2 New Data in Migration Research
		18.3 New Opportunities in Migration Research
		18.4 The Way Forward
		References
	19 New Data and Computational Methods Opportunities to Enhance the Knowledge Base of Tourism
		19.1 Introduction
		19.2 Existing Literature
			19.2.1 New Data Sources of Potential Interest for Tourism
			19.2.2 New Computational Methods with Application to Tourism Studies
		19.3 Guidelines
			19.3.1 Assessing the Environmental Impacts of Tourism
			19.3.2 Socio-Economic Resilience in the Tourism Sector
			19.3.3 Uncovering New Tourists\' Preferences, Digital Transition and Innovation in the Tourism Sector
			19.3.4 Analysis of Preference Changes in the Tourism Sector
				19.3.4.1 Searching for Holidays and Activities
				19.3.4.2 Text Is a Mine
				19.3.4.3 What a Beautiful Picture!
				19.3.4.4 Life Is Change
			19.3.5 Digital Transition and Innovation
				19.3.5.1 What Are the Main Challenges for Increasing Digitalisation and Innovation in the Tourism Sector? How Can Existing Difficulties Be Overcome?
				19.3.5.2 What Are the Main Difficulties in Collecting New Data? What Strategies Towards Effective Data Collection Should Be Put in Place?
				19.3.5.3 How to Measure Innovation, Digital Transition and Digital Skills Needs in the Tourism Ecosystem?
				19.3.5.4 How to Motivate and Monitor High-Quality Data Collection by the EU Member States?
		19.4 The Way Forward
		References
	20 Computational Social Science for Policy and Quality of Democracy: Public Opinion, Hate Speech, Misinformation, and Foreign Influence Campaigns
		20.1 Introduction
		20.2 Computational Social Science and Measuring Public Opinion
		20.3 Computational Social Science and Hate Speech
		20.4 Computational Social Science and Misinformation
		20.5 Computational Social Science and Coordinated Foreign Influence Operations
		20.6 The Importance of External Data Access
		References
	21 Social Interactions, Resilience, and Access to Economic Opportunity: A Research Agenda for the Field of Computational Social Science
		21.1 Introduction
		21.2 Current Progress
			21.2.1 Online Social Networking Services
			21.2.2 Other Communication Networks
			21.2.3 Financial or Business Transaction Networks
			21.2.4 Civic Networks
		21.3 The Way Forward
			21.3.1 Increasing Access to Microdata
			21.3.2 Increasing Access to Aggregated Data
		21.4 Summary
		References
	22 Social Media Contribution to the Crisis Management Processes: Towards a More Accurate Response Integrating Citizen-Generated Content and Citizen-Led Activities
		22.1 Introduction
		22.2 State of the Art
			22.2.1 Social Media and Crisis Management: New Perspectives
			22.2.2 From Citizen-Generated Content to Citizen-Led Activities: Opportunities and Challenges
		22.3 Computational Guidelines
			22.3.1 Which Contribution to the Crisis Management Cycle?
			22.3.2 Towards an Actionable Information for Practitioners
				22.3.2.1 Designing Automatic Emergency Systems to Support Local EMS and EU Supervision: Directions
			22.3.3 Integrating Citizen-Led Activities in the Crisis Management Processes
				22.3.3.1 Social Media as a Communication and Organizational Infrastructure
				22.3.3.2 Citizens: First Links of the Crisis Management Chain?
				22.3.3.3 Building Specific Partnerships and Collaborations with Existing Online Communities
		22.4 The Way Forward
		References
	23 The Empirical Study of Human Mobility: Potentials and Pitfalls of Using Traditional and Digital Data
		23.1 Introduction
		23.2 Monitoring Human Mobility: Traditional and New Data
			23.2.1 Traditional Data: Pros and Cons
			23.2.2 Non-traditional Data Usages: An Overview
				23.2.2.1 A Review of the Usefulness of Non-traditional Data to Study Different Types of Mobility
				23.2.2.2 Limitations and Caveats in the Use of Non-traditional Data on Human Mobility
		23.3 Concluding Remarks
		References
	24 Towards a More Sustainable Mobility
		24.1 Introduction
		24.2 Background: Computational, Environmental, and Data Aspects of Sustainable Mobility Technologies
			24.2.1 Hybrid and Plug-In Vehicles
			24.2.2 Connected Autonomous Vehicles (CAV)
			24.2.3 Compressed Natural Gas (CNG) Vehicles
			24.2.4 Hydrogen Fuel Cell Vehicles
			24.2.5 Unmanned Aerial Vehicles (UAVs)
			24.2.6 Carsharing
			24.2.7 Micromobility
				24.2.7.1 Cycling and Electric Bikes
				24.2.7.2 Electric Scooter
				24.2.7.3 Mobility as a Service (MaaS)
		24.3 Questions and Challenges: Decarbonisation of the Transport Sector with the Currently Available Technology
		24.4 Impact of the Pandemic
		24.5 Developing Countries
		24.6 Policy Restrictions
		24.7 Conclusions and Recommendations
		References
Conclusions: Status and a Way Forward for Computational Social Science in Policymaking
	References