Book contents
- Frontmatter
- Contents
- Preface
- Introduction
- PART 1 COMPUTATIONAL SOCIAL SCIENCE TOOLS
- PART 2 computational social science applications
- 7 Big Data, Social Media, and Protest: Foundations for a Research Agenda
- 8 Measuring Representational Style in the House: The Tea Party, Obama, and Legislators’ Changing Expressed Priorities
- 9 Using Social Marketing and Data Science to Make Government Smarter
- 10 Using Machine Learning Algorithms to Detect Election Fraud
- 11 Centralized Analysis of Local Data, with Dollars and Lives on the Line: Lessons from the Home Radon Experience
- Conclusion Computational Social Science: Toward a Collaborative Future
- Index
9 - Using Social Marketing and Data Science to Make Government Smarter
from PART 2 - computational social science applications
Published online by Cambridge University Press: 05 March 2016
- Frontmatter
- Contents
- Preface
- Introduction
- PART 1 COMPUTATIONAL SOCIAL SCIENCE TOOLS
- PART 2 computational social science applications
- 7 Big Data, Social Media, and Protest: Foundations for a Research Agenda
- 8 Measuring Representational Style in the House: The Tea Party, Obama, and Legislators’ Changing Expressed Priorities
- 9 Using Social Marketing and Data Science to Make Government Smarter
- 10 Using Machine Learning Algorithms to Detect Election Fraud
- 11 Centralized Analysis of Local Data, with Dollars and Lives on the Line: Lessons from the Home Radon Experience
- Conclusion Computational Social Science: Toward a Collaborative Future
- Index
Summary
It is generally agreed that the most important single function of government is to secure the rights and freedoms of individual citizens. Adam Smith, the eighteenth-century philosopher, was restrictive in his view of these rights, limiting them essentially to three basic government functions: protection of the nation, administration of law and order, and the provision of certain public functions (e.g., infrastructure and education; Smith, 1976). Although the role of government and the process by which it is executed vary greatly, the benefits provided to the citizens of countries with well-managed government systems are clear. Countries with more effective governments are more likely to obtain better credit ratings, attract more investment, offer higher quality public services, encourage higher levels of human capital accumulation, effectively utilize foreign aid resources, accelerate technological innovation, and increase the productivity of government spending (Burnside & Dollar, 2000; Baum & Lake, 2003). Further, countries with more effective governments have better educational systems and more efficient health care (Lewis, 2006; Baldacci, Clements, Gupta, & Cui, 2004).
In the past when government agencies have wanted to institute change or influence citizen behavior, they often relied on a common set of educational or policy initiatives. These tools rely on a broad-brush approach aimed at developing knowledge, reinforcing desired behaviors through funding initiatives and/or tax incentives, or requiring change through the development of new regulatory measures or laws. Although these uniform, rational, and compliance methods have their place, technological and societal shifts have required and provided opportunities for government and public policy to adapt. Society has increasingly pushed for government to become more goal-oriented, aiming for measurable results that can be achieved with some degree of immediacy (Geurts, 2010). Furthermore, technological changes have created opportunities for agencies to rely more heavily on advanced statistical methods with social marketing principles to seamlessly integrate targeted communication efforts with their more traditional tools to bring about behavioral change.
At the core of the government's ability to effectively shift to more targeted, efficient, and outcome-oriented initiatives is the exponentially increasing amount of administrative- and/or survey-based information available in electronic format. This information has taken on the general moniker of “big data” as a singular definition, but the respects from which it can be big vary meaningfully, affecting how it can be approached and its ultimate utility.
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- Computational Social ScienceDiscovery and Prediction, pp. 246 - 265Publisher: Cambridge University PressPrint publication year: 2016
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