Governance Democratic and Big Data: A Systematic Mapping Review
Abstract
:1. Introduction
2. Background
2.1. Democratic Governance and Big Data
2.2. Open Government e-Government
2.3. Data Governance and Sustainability
3. Related Works
4. Materials and Methods
4.1. Goal and Research Questions
4.2. Research Questions to Be Applied
4.3. Data Extraction
4.4. Inclusion and Exclusion Criteria
4.4.1. Inclusion Criteria
- Articles published in English from journals and conferences.
- Full articles considering democratic governance and Big Data.
- Studies as of 2010.
4.4.2. Exclusion Criteria
- Articles not in English.
- Articles before 2010.
- Articles that do not include topics related to democratic governance and Big Data.
- Duplicate studies in different databases.
- Incomplete items.
- Articles that are not in Journals or Conferences.
- Articles that are not available (open access).
- Items not relevant in the search string.
- Reviews (can be used in Related Works).
- Gray literature articles that are not in official government sources.
4.5. Search Execution
4.6. Classification Scheme
- Analysis: We refer to papers describing analyses and comparisons of the literature on democratic governance and Big Data.
- Use: Corresponds to studies or work related to democratic governance and Big Data for further application.
- Implementation: Proposed solutions for democratic governance and Big Data. Articles can be classified into more than one article type.
4.7. Map Construction
5. Results
5.1. Overall Analysis by Characteristics
- Articles describing analyses and comparisons of the literature on democratic governance and big data.
- Articles that use proposals in democratic governance and big data for further application.
- Articles that propose solutions for democratic governance and big data.
5.2. Systematic Mapping
5.2.1. Responding to Research Questions
- RQ1: Within the democratic governance and Big Data literature, what types of papers have been presented in the literature?
- RQ2: Of the initiatives encountered, in what context has the work been carried out?
- RQ3: How many democratic governance initiatives have been implemented in different nations using Big Data?
- RQ4: Of the systems presented, how many feature Big Data architectures?
- RQ5: Of the architectures presented, which ones comply as Big Data architectures according to the authors?
- RQ6: What kind of technologies are used in the development of Big Data systems associated with democratic governance?
- RQ7: What are the current challenges of democratic governance using Big Data?
5.2.2. Answers to Publication Questions
- PQ1: What are the sources in which democratic governance initiatives using Big Data have been published?
- PQ2: How have these initiatives emerged and evolved over the years?
- PQ3: Which countries have the highest concentration of publications in the area of democratic governance?
6. Discussion
Research Gap
7. Proposal
8. Limitations of the Study
8.1. Potential Threats
8.2. Internal Validity
8.3. External Validity
8.4. Accuracy and Validity
9. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Num. | Article | Cite | Brief Description |
---|---|---|---|
1 | `Hypernudge’: Big Data as a Mode of Regulation by Design | [82] | The concept of “Hypernudge” as a form of regulation based on the use of Big Data to guide algorithmic decisions is discussed. The pros and cons of this approach to regulation are discussed, including privacy, transparency, and accountability concerns. The importance of policy makers establishing ethical regulation to protect individual rights is emphasized. |
2 | Advertising, Big Data, and the Clearance of the Public Realm: Marketers’ New Approaches to the Content Subsidy | [83] | It discusses how the targeting of consumers for advertising and the new data capture industry can affect the subsidy of media content production and erode the civic sphere, which has consequences for democracy. The importance of maintaining democracy in this context is highlighted. |
3 | Artificial Intelligence and Data Governance for Precision ePolicy Cycle | [84] | An AI can transform the public policy cycle, enabling precision policies and accelerating decision making. This would improve democracy by enabling the use of Big Data, giving rise to a new digital public policy cycle and qualifying democracy and administration as precision democracy. |
4 | Asian Newsrooms in Transition: A Study of Data Journalism Forms and Functions in Singapore’s State-Mediated Press System | [85] | It looks at data journalism in Asian newsrooms, where it seeks to enhance the public’s experience as a consumer and political data stories transcend national boundaries. Data stories do not need investigative elements or watchdog papers to be exceptional; they can be informative in a less adversarial way. |
5 | Big Data Analytics in E-Government and E-Democracy Applications: Privacy Threats, Implications and Mitigation | [86] | The text discusses the benefits and threats of the use of Big Data in democratic processes and its implications for privacy and democracy. There are different ways to mitigate threats to privacy while benefiting from the use of Big Data. |
6 | Big Data Analytics: From Threatening Privacy to Challenging Democracy | [87] | Political scientists face challenges and opportunities due to the large volume of data generated on the Internet, and the use of business intelligence and analytics tools for political purposes, which may raise privacy concerns. The lack of privacy-focused studies on the practice of targeting voters with personalized messages is highlighted. |
7 | Big Data and Dahl’s Challenge of Democratic Governance | [71] | The text analyzes the impact of Big Data on democratic governance in three policy areas, using Robert Dahl’s dimensions of control and autonomy and highlighting their potential tensions. Although Big Data applications in the public sector have great transformative potential, most theory is narrowly focused on technocratic objectives. |
8 | Big Data and Democracy: Facts and Values | [88] | It should shift the ethical debate in political science towards a collective framework of democratic values and examines the production of data in social networks, highlighting the need for political scientists to be aware of democratic values when working with them. Overall, it argues for a broader debate on the democratization of data. |
9 | Big Data and the Phantom Public: Walter Lippmann and the Fallacy of Data Privacy Self-Management | [89] | The text analyzes the fallacy of self-management of data privacy in the context of Big Data and the digital citizen. Drawing on Walter Lippmann’s theory, the author opines that data privacy perpetuates problems in a number of areas. He offers a critical perspective on current policies related to data privacy. |
10 | Big Data Ethics and Selection-Bias: An Official Statistician’s Perspective | [74] | The use of Big Data as a source of official statistics is explored, highlighting ethical and statistical concerns, and suggests that it could be a cost-effective solution. The importance of seeking new ways to produce relevant and frequent statistical information is highlighted, and it is made clear that the views are those of the authors and do not necessarily represent the views of the Australian Bureau of Statistics or other organizations. |
11 | Big Data Governance and Representative Democracy [Gobernanza de los Macrodatos y Democracia Representativa] | [90] | The text discusses the relationship between Big Data governance and representative democracy, highlighting the importance of an ethical framework that protects democratic values. It emphasizes the need to consider privacy and civil liberties when using this data and the importance of values such as transparency, accountability, and participation in the development of such an ethical framework. |
12 | Big Data in Political Communication | [91] | The use of Big Data is transforming political communication through political marketing and microtargeting. However, political campaigns cannot rely exclusively on data and Big Data consultants to be effective. It highlights the limits and democratic concerns associated with its use. |
13 | Big Data, Algorithmic Regulation, and the History of the Cybersyn Project in Chile, 1971–1973 | [92] | The Cybersyn project was a technology system that addressed issues similar to algorithmic regulation and Big Data. There are several potential benefits of dynamic data-driven regulation, but there are also concerns about data storage and centralization. |
14 | Campaign Strategies, Media, and Voters: The Fourth Era of Political Communication | [93] | The discussion includes examples from political campaigns in different contexts, demonstrating the increasing sophistication of strategists in using online tools to collect voter data, manage campaign resources, mobilize voters, generate new information, and improve the effectiveness of political communication. |
15 | Cape Town as a Smart and Safe City: Implications for Governance and Data Privacy | [94] | Case study of Cape Town’s “smart and safe” cities program, where the implications of using “smart city” technologies for data governance and privacy are explored. Cape Town’s overall smart city framework and its role in achieving the UN Sustainable Development Goals are analyzed. |
16 | Constructing a Public Narrative of Regulations for Big Data and Analytics: Results From a Community-Driven Discussion | [95] | Community perspectives on the regulation of municipally-led Big Data initiatives. While data analytics holds great promise, its mythologized nature can lead to blind faith in empirical results, which can result in the omission or misrepresentation of marginalized populations. |
17 | Data Politics: Worlds, Subjects, Rights | [96] | Data has become a social and political issue because of its ability to reshape the relationships between states, subjects, and citizens. Data and politics are now inseparable; data are not only shaping our social relations, preferences, and life chances, but also our democracies themselves. Without understanding these conditions of possibility, it is impossible to intervene in or shape the politics of data. |
18 | Data-Driven Authoritarianism: Non-Democracies and Big Data | [97] | This chapter examines how authoritarian regimes use Big Data to maintain political and social control. These regimes also use advanced propaganda techniques, such as audience segmentation and message personalization, to influence public opinion and perpetuate their regime. |
19 | Datatrust: Or, the Political Quest for Numerical Evidence and the Epistemologies of Big Data | [98] | The current trend of evidence-based policy making through the use of Big Data is what is discussed in this article. It highlights the dependence of data exaggeration on specific forms of trust, truth, and objectivity; the analysis of the historical roots of the current Big Data phenomenon; and its relationship to the ideal of mechanical objectivity. |
20 | Deep Learning for Deepfakes Creation and Detection: A Survey | [99] | Deepfakes are created using machine learning algorithms to generate realistic images or videos that can be used to spread misinformation or mislead people. Detecting deepfakes is not a straightforward issue and there are various techniques such as using machine learning algorithms to analyze the content and identify inconsistencies. |
21 | Democracy Under Attack: Challenges of Addressing Ethical Issues of AI and Big Data for More Democratic Digital Media and Societies | [100] | The paper examines the ethical implications of digital media, Big Data, and artificial intelligence on democracy and human rights. Challenges include reconciling heterogeneous perspectives on ethics, increasing polarization and populism, and engineers’ lack of critical self-reflection. |
22 | Digital Politics, GDPR, and AI | [101] | Here we examine how the European Union has addressed the impact of artificial intelligence on policy, economics, and law, and how the EU General Data Protection Regulation affects the collection and use of data in digital policy. |
23 | Paradigm Shifts from E-Governance to S-Governance | [102] | This article describes the paradigm shift in e-governance from a technology-centered approach to a people-centered approach. It argues that e-governance must evolve towards social governance to ensure a more inclusive and effective participation of citizens in political decision making. |
24 | The Innovative State | [103] | Big data can increase the epistemic and sensory capacity of agencies, allowing them to gain a more detailed understanding of conditions on the ground with the participation of a more diverse audience. |
25 | The Organizational Structure and Operational Logic of an Urban Smart Governance Information Platform: Discussion on the Background of Urban Governance Transformation in China | [104] | The research analyzing the organizational structure and operational logic of a smart urban governance information platform in China aims to identify the trends of urban governance transformation in China and design a governance organizational structure based on the smart urban governance information platform. |
26 | Towards a Political Theory of Data Justice: A Public Good Perspective | [105] | Interdisciplinary study proposing a political theory of data justice from a public good perspective. The study connects three political theories of the public good with empirical studies on the functions of big data to guide and constrain state data practices that aim to minimize political abuse of data power. |
27 | Towards Better Environmental Governance in Taiwan | [106] | The chapter highlights the importance of an integrated approach to address environmental challenges, involving government, markets, and civil society. It emphasizes the use of new technologies, such as IoT and geographic information systems, for innovation. This approach is illustrated with two concrete examples: greenhouse gas emissions reduction and solid waste management. |
28 | Visualization Practice and Government: Strategic Investments for More Democratic Governance | [107] | Data visualization has great potential to illuminate complex challenges and create collective knowledge. It can be used to efficiently share information and show the results of meetings and data analysis. In the public sector, its ability to describe and analyze complex challenges, monitor social networks, and communicate information to leaders and the general public is highlighted. |
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Research Questions | |
---|---|
Research Question | Motivation |
RQ1: Within the democratic governance and Big Data writings found, what types of papers have been presented in the literature? | Understand the different approaches and trends in the literature related to the intersection between democracy, governance, and technology. It is expected to broaden the understanding of democratic governance and Big Data and explore new ways of applying technology to improve democratic governance. |
RQ2: Of the initiatives encountered, in what context has the work been developed? | Identify patterns and emerging trends that will guide future research and explore new ways to apply technology to improve democratic governance. |
RQ3: How many democratic governance initiatives have been implemented in different nations using Big Data? | Knowing the number of democratic governance initiatives that have used Big Data in different countries is key to understanding the relevance of the technology in this area and identifying patterns and trends that can improve the implementation of future projects. |
RQ4: Of the systems presented, how many feature Big Data architectures? | Identify how many feature Big Data architectures, which may indicate an emerging trend in applying technology in democratic governance. Identify patterns and trends that allow a better understanding of how these technologies are applied in democratic governance, along with contributing to informing future democratic governance projects using Big Data technologies. |
RQ5: Of the architectures presented, which ones comply with Big Data architectures according to the authors? | In investigating the architectures presented, it is necessary to identify which meet the criteria to be considered true Big Data architectures. In doing so, it is possible to identify emerging trends in the use of Big Data technologies in democratic governance and limitations and challenges in implementing these technologies. |
RQ6: What are the results of new methods or models for ensuring security in secure software development? | Identify what types of technologies are being used in their development. By doing so, one can identify the most effective and efficient tools for developing future projects and the limitations and challenges associated with their implementation. In addition, by comparing the technologies used in different systems, patterns and emerging trends in applying Big Data technologies in democratic governance can be identified. Contribute to informing and improving the implementation of future democratic governance projects using Big Data technologies. |
RQ7: What are the current challenges of democratic governance using Big Data? | Identify the limitations and challenges of implementing Big Data technologies in this area. By understanding these challenges, more effective solutions and approaches can be developed to address these problems. This research can help inform and improve the implementation of future democratic governance projects that use Big Data technologies ethically and effectively. |
Publication Questions | |
---|---|
Research Question | Motivation |
PQ1: What are the sources in which democratic governance initiatives using Big Data have been published? | Academic publications, government reports, and other sources relevant to the field can be identified. In doing so, emerging trends and best practices in applying Big Data technologies in democratic governance can be identified. In addition, by exploring these sources, opportunities for future research and collaborations can be identified. |
PQ2: How have these initiatives emerged and evolved over the years? | To identify emerging trends and changes in the technologies and tools used in applying Big Data to democratic governance. Furthermore, by doing so, emerging challenges and opportunities in this field can be identified, as well as the impacts and effects of these initiatives on democratic governance. Ultimately, this research can help inform and improve the implementation of future democratic governance projects using Big Data technologies. |
PQ3: Which are the countries with the highest concentration of publications in the area of democratic governance | Knowing which countries have the most publications in the area of democratic governance can help to recognize countries on which to focus research in non-academic sources, as well as to look for future patterns associated with good democratic governance through the use of Big Data. |
Main Concepts | democratic governance, democratic government, democracy governance, democracy government, democracy, e-democracy, e democracy, edemocracy, Big Data. |
Groups of terms | (“democratic governance” OR “democratic government” OR “democracy governance” OR “democracy government” OR “democracy” OR “e-democracy” OR “e democracy” OR “edemocracy”). “big data”. |
Search String | (“democratic governance” OR “democratic government” OR “democracy governance” OR “democracy government” OR “democracy” OR “e-democracy” OR “e democracy” OR “edemocracy”) AND “big data”) |
Data Source | Abstract Selection |
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Web of Science | 55 |
Scopus | 146 |
Google Scholar | 92 |
Total | 293 |
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Hochstetter-Diez, J.; Negrier-Seguel, M.; Diéguez-Rebolledo, M.; Vásquez-Morales, F.; Sancho-Chavarría, L. Governance Democratic and Big Data: A Systematic Mapping Review. Sustainability 2023, 15, 12630. https://doi.org/10.3390/su151612630
Hochstetter-Diez J, Negrier-Seguel M, Diéguez-Rebolledo M, Vásquez-Morales F, Sancho-Chavarría L. Governance Democratic and Big Data: A Systematic Mapping Review. Sustainability. 2023; 15(16):12630. https://doi.org/10.3390/su151612630
Chicago/Turabian StyleHochstetter-Diez, Jorge, Marlene Negrier-Seguel, Mauricio Diéguez-Rebolledo, Felipe Vásquez-Morales, and Lilliana Sancho-Chavarría. 2023. "Governance Democratic and Big Data: A Systematic Mapping Review" Sustainability 15, no. 16: 12630. https://doi.org/10.3390/su151612630