Big Data and Social Science

The goal of this chapter is to explore the practice of big data sharing among academics and issues related to this sharing. The first part of the chapter reviews literature on big data sharing practices using current technology. The second part presents case studies on disciplinary data repositories in terms of their requirements and policies. It describes and compares such requirements and policies at disciplinary repositories in three areas: Dryad for life science, Interuniversity Consortium for Political and Social Research (ICPSR) for social science, and the National Oceanographic Data Center (NODC) for physical science.

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An Introduction to Computational Social Science for Organizational Communication

Transformative Practice and Research in Organizational Communication - Advances in Business Strategy and Competitive Advantage ◽

10.4018/978-1-5225-2823-4.ch011 ◽

2018 ◽

pp. 184-200

Author(s):

Andrew N. Pilny ◽

Marshall Scott Poole

Keyword(s):

Big Data ◽

Social Science ◽

Organizational Communication ◽

Computational Social Science ◽

Lessons Learned ◽

Audit Culture ◽

Research Designs ◽

Data Driven Approach ◽

Interaction Modeling ◽

The Relationship

The exponential growth of “Big Data” has given rise to a field known as computational social science (CSS). The authors view CSS as the interdisciplinary investigation of society that takes advantage of the massive amount of data generated by individuals in a way that allows for abductive research designs. Moreover, CSS complicates the relationship between data and theory by opening the door for a more data-driven approach to social science. This chapter will demonstrate the utility of a CSS approach using examples from dynamic interaction modeling, machine learning, and network analysis to investigate organizational communication (OC). The chapter concludes by suggesting that lessons learned from OC's history can help deal with addressing several current issues related to CSS, including an audit culture, data collection ethics, transparency, and Big Data hubris.

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The Role of Big Data in Social Science: A Case Study Using Hadoop

10.1007/978-981-16-1781-2_23 ◽

2021 ◽

pp. 243-251

Author(s):

Nour Alqudah ◽

Mohammed Q. Shatnawi

Keyword(s):

Big Data ◽

Social Science

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‘Big Social Science': Doing Big Data in the Social Sciences

The SAGE Handbook of Online Research Methods ◽

10.4135/9781473957992.n8 ◽

2017 ◽

pp. 125-139 ◽

Cited By ~ 3

Author(s):

Jonathan Bright

Keyword(s):

Social Sciences ◽

Big Data ◽

Social Science ◽

The Social

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A Critical Review of the Big-Data Paradigm

International Journal of Risk and Contingency Management ◽

10.4018/ijrcm.2016070104 ◽

2016 ◽

Vol 5 (3) ◽

pp. 46-59

Author(s):

Ruben Xing ◽

Jinluan Ren ◽

Jianghua Sun ◽

Lihua Liu

Keyword(s):

Information Technology ◽

Big Data ◽

Internet Of Things ◽

Social Science ◽

Critical Review ◽

New Technology ◽

Business Leaders ◽

Transitional Period ◽

Strategic Plans ◽

Strategic Changes

The moving directions of big data are readjusted with updated concerns along with the quick boom of Internet of Things (IoT). Any serious contribution to the advance of the IoT must necessarily be the result of synergetic activities conducted in different fields of knowledge, such as telecommunications, informatics, electronics and social science. Big data was a hot topic in past years. It is not a new technology, but a huge resource generated from those fields. Some of the omitted focuses become major strategic plans for developers, and several new functions are becoming critical needs for the smart Internet movement. This paper is to address big data with the strategic changes and directions during the sensitive transitional period to be recognized for the business leaders and information technology (IT) developers.

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