scholarly journals Enhancing Big Data in the Social Sciences with Crowdsourcing: Data Augmentation Practices, Techniques, and Opportunities

2017 ◽  
Author(s):  
Nathaniel D. Porter ◽  
Ashton Verdery ◽  
S. Michael Gaddis
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Data Augmentation ◽  
The Social

scholarly journals Enhancing big data in the social sciences with crowdsourcing: Data augmentation practices, techniques, and opportunities

PLoS ONE ◽  
2020 ◽  
Vol 15 (6) ◽  
pp. e0233154
Author(s):  
Nathaniel D. Porter ◽  
Ashton M. Verdery ◽  
S. Michael Gaddis
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Data Augmentation ◽  
The Social

scholarly journals Big Data: Some Ethical Concerns for the Social Sciences

2021 ◽  
Vol 10 (2) ◽  
pp. 36
Author(s):  
Michael Weinhardt
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Ethical Issues ◽  
Online Behavior ◽  
Ethical Concerns ◽  
Ethical Implications ◽  
The Social ◽  
And Behaviors ◽  
State Actors

While big data (BD) has been around for a while now, the social sciences have been comparatively cautious in its adoption for research purposes. This article briefly discusses the scope and variety of BD, and its research potential and ethical implications for the social sciences and sociology, which derive from these characteristics. For example, BD allows for the analysis of actual (online) behavior and the analysis of networks on a grand scale. The sheer volume and variety of data allow for the detection of rare patterns and behaviors that would otherwise go unnoticed. However, there are also a range of ethical issues of BD that need consideration. These entail, amongst others, the imperative for documentation and dissemination of methods, data, and results, the problems of anonymization and re-identification, and the questions surrounding the ability of stakeholders in big data research and institutionalized bodies to handle ethical issues. There are also grave risks involved in the (mis)use of BD, as it holds great value for companies, criminals, and state actors alike. The article concludes that BD holds great potential for the social sciences, but that there are still a range of practical and ethical issues that need addressing.

scholarly journals Bought and Sold: Exploring the Effects of Big Data on User Agency and Commodification

2021 ◽  
Author(s):  
Kristia M. Pavlakos
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Data Privacy ◽  
Large Data ◽  
Data Sets ◽  
Privacy Regulation ◽  
Scholarly Literature ◽  
User Interests ◽  
The Social ◽  
The Relationship

Big Data1is a phenomenon that has been increasingly studied in the academy in recent years, especially in technological and scientific contexts. However, it is still a relatively new field of academic study; because it has been previously considered in mainly technological contexts, more attention needs to be drawn to the contributions made in Big Data scholarship in the social sciences by scholars like Omar Tene and Jules Polonetsky, Bart Custers, Kate Crawford, Nick Couldry, and Jose van Dijk. The purpose of this Major Research Paper is to gain insight into the issues surrounding privacy and user rights, roles, and commodification in relation to Big Data in a social sciences context. The term “Big Data” describes the collection, aggregation, and analysis of large data sets. While corporations are usually responsible for the analysis and dissemination of the data, most of this data is user generated, and there must be considerations regarding the user’s rights and roles. In this paper, I raise three main issues that shape the discussion: how users can be more active agents in data ownership, how consent measures can be made to actively reflect user interests instead of focusing on benefitting corporations, and how user agency can be preserved. Through an analysis of social sciences scholarly literature on Big Data, privacy, and user commodification, I wish to determine how these concepts are being discussed, where there have been advancements in privacy regulation and the prevention of user commodification, and where there is a need to improve these measures. In doing this, I hope to discover a way to better facilitate the relationship between data collectors and analysts, and user-generators. 1 While there is no definitive resolution as to whether or not to capitalize the term “Big Data”, in capitalizing it I chose to conform with such authors as boyd and Crawford (2012), Couldry and Turow (2014), and Dalton and Thatcher (2015), who do so in the scholarly literature.

scholarly journals O fim da teoria: o confronto entre a pesquisa orientada por dados e a pesquisa orientada por hipóteses | The end of theory: the confrontation between data-driven research and hypothesis-driven research

2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Luís Fernando Sayão ◽  
Luana Farias Sales
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Scientific Method ◽  
Computational Analysis ◽  
Data Driven ◽  
Present Essay ◽  
Contemporary Science ◽  
The Social ◽  
Analysis Strategies ◽  
New Knowledge

RESUMO A ciência contemporânea e seus fundamentos metodológicos têm sido impactados pelo fenômeno do big data, que proclama que na era dos dados medidos em petabytes, de supercomputadores e sofisticados algoritmos, o método científico está obsoleto e que as hipóteses e modelos estão superados. As estratégias do big data científico confia em estratégias de análises computacionais de massivas quantidades de dados para revelar correlações, padrões e regras que vão gerar novos conhecimentos, que vão das ciências exatas até as ciências sociais, humanidade e cultura, delineando um arquétipo de ciência orientada por dados. O presente ensaio coloca em pauta as controvérsias em torno da ciência orientada por dados em contraposição à ciência orientada por hipóteses, e analisa alguns dos desdobramentos desse embate epistemológico. Para tal, tomo como metodologia os escritos de alguns autores mais proximamente envolvidos nessa questão.Palavras-chave: Big Data; Método Cientifico; Ciência Orientada por Dados; Ciência Orientada por Hipóteses.ABSTRACT Contemporary science and its methodological foundations have been impacted by the big data phenomenon that proclaims that in the age of data measured in petabytes, supercomputers and sophisticated algorithms the scientific method is obsolete and that the hypotheses and models are outdated.The strategies of the scientific big data rely on computational analysis strategies of massive amounts of data to reveal correlations, patterns and rules that will generate new knowledge, ranging from the exact sciences to the social sciences, humanity and culture, outlining an archetype of data-driven science. The present essay addresses the debates around data-driven science as opposed to hypothesis-oriented science and analyzes some of the ramifications of this epistemological confrontation. For this, the writings of some authors who are more closely involved in this question are taken as methodology.Keywords: Big Data; Scientific Method; Data-Driven Science; Hypothesis-Driven Science.

scholarly journals Bought and Sold: Exploring the Effects of Big Data on User Agency and Commodification

2021 ◽  
Author(s):  
Kristia M. Pavlakos
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Data Privacy ◽  
Large Data ◽  
Data Sets ◽  
Privacy Regulation ◽  
Scholarly Literature ◽  
User Interests ◽  
The Social ◽  
The Relationship

Big Data1is a phenomenon that has been increasingly studied in the academy in recent years, especially in technological and scientific contexts. However, it is still a relatively new field of academic study; because it has been previously considered in mainly technological contexts, more attention needs to be drawn to the contributions made in Big Data scholarship in the social sciences by scholars like Omar Tene and Jules Polonetsky, Bart Custers, Kate Crawford, Nick Couldry, and Jose van Dijk. The purpose of this Major Research Paper is to gain insight into the issues surrounding privacy and user rights, roles, and commodification in relation to Big Data in a social sciences context. The term “Big Data” describes the collection, aggregation, and analysis of large data sets. While corporations are usually responsible for the analysis and dissemination of the data, most of this data is user generated, and there must be considerations regarding the user’s rights and roles. In this paper, I raise three main issues that shape the discussion: how users can be more active agents in data ownership, how consent measures can be made to actively reflect user interests instead of focusing on benefitting corporations, and how user agency can be preserved. Through an analysis of social sciences scholarly literature on Big Data, privacy, and user commodification, I wish to determine how these concepts are being discussed, where there have been advancements in privacy regulation and the prevention of user commodification, and where there is a need to improve these measures. In doing this, I hope to discover a way to better facilitate the relationship between data collectors and analysts, and user-generators. 1 While there is no definitive resolution as to whether or not to capitalize the term “Big Data”, in capitalizing it I chose to conform with such authors as boyd and Crawford (2012), Couldry and Turow (2014), and Dalton and Thatcher (2015), who do so in the scholarly literature.

Thick Big Data

2020 ◽  
Author(s):  
Dariusz Jemielniak
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Data Access ◽  
Cultural Analysis ◽  
Additional Advantage ◽  
Corporate World ◽  
New Methods ◽  
Novel Approach ◽  
The Social ◽  
History Of

The social sciences are becoming datafied. The questions that have been considered the domain of sociologists, now are answered by data scientists, operating on large datasets, and breaking with the methodological tradition for better or worse. The traditional social sciences, such as sociology or anthropology, are thus under the double threat of becoming marginalized or even irrelevant; both because of the new methods of research, which require more computational skills, and because of the increasing competition from the corporate world, which gains an additional advantage based on data access. However, sociologists and anthropologists still have some important assets, too. Unlike data scientists, they have a long history of doing qualitative research. The more quantified datasets we have, the more difficult it is to interpret them without adding layers of qualitative interpretation. Big Data needs Thick Data. This book presents the available arsenal of new tools for studying the society quantitatively, but also show the new methods of analysis from the qualitative side and encourages their combination. In shows that Big Data can and should be supplemented and interpreted through thick data, as well as cultural analysis, in a novel approach of Thick Big Data.The book is critically important for students and researchers in the social sciences to understand the possibilities of digital analysis, both in the quantitative and qualitative area, and successfully build mixed-methods approaches.

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