Computational Social Science and Sociology

The integration of social science with computer science and engineering fields has produced a new area of study: computational social science. This field applies computational methods to novel sources of digital data such as social media, administrative records, and historical archives to develop theories of human behavior. We review the evolution of this field within sociology via bibliometric analysis and in-depth analysis of the following subfields where this new work is appearing most rapidly: ( a) social network analysis and group formation; ( b) collective behavior and political sociology; ( c) the sociology of knowledge; ( d) cultural sociology, social psychology, and emotions; ( e) the production of culture; ( f) economic sociology and organizations; and ( g) demography and population studies. Our review reveals that sociologists are not only at the center of cutting-edge research that addresses longstanding questions about human behavior but also developing new lines of inquiry about digital spaces as well. We conclude by discussing challenging new obstacles in the field, calling for increased attention to sociological theory, and identifying new areas where computational social science might be further integrated into mainstream sociology.

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For a heterodox computational social science

Big Data & Society ◽

10.1177/20539517211047725 ◽

2021 ◽

Vol 8 (2) ◽

pp. 205395172110477

Author(s):

Petter Törnberg ◽

Justus Uitermark

Keyword(s):

Complex System ◽

Social Science ◽

Computational Methods ◽

Social Life ◽

Critical Realism ◽

Computational Social Science ◽

Digital Data ◽

Neoclassical Economics ◽

Research Strategies ◽

Digital Capitalism

The proliferation of digital data has been the impetus for the emergence of a new discipline for the study of social life: ‘computational social science’. Much research in this field is founded on the premise that society is a complex system with emergent structures that can be modeled or reconstructed through digital data. This paper suggests that computational social science serves practical and legitimizing functions for digital capitalism in much the same way that neoclassical economics does for neoliberalism. In recognition of this homology, this paper develops a critique of the complexity perspective of computational social science and argues for a heterodox computational social science founded on the meta-theory of critical realism that is critical, methodological pluralist, interpretative and explanative. This implies diverting computational social science’ computational methods and digital data so as to not be aimed at identifying invariant laws of social life, or optimizing state and corporate practices, but to instead be used as part of broader research strategies to identify contingent patterns, develop conjunctural explanations, and propose qualitatively different ways of organizing social life.

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Big Data and the Study of Communities and Crime

Oxford Research Encyclopedia of Criminology and Criminal Justice ◽

10.1093/acrefore/9780190264079.013.318 ◽

2018 ◽

Author(s):

Daniel T. O'Brien

Keyword(s):

Social Media ◽

Big Data ◽

Computational Social Science ◽

Data Sources ◽

Digital Data ◽

Data Sets ◽

Popular Imagination ◽

Administrative Records ◽

Communities And Crime ◽

Criminology And Criminal Justice

In recent years, a variety of novel digital data sources, colloquially referred to as “big data,” have taken the popular imagination by storm. These data sources include, but are not limited to, digitized administrative records, activity on and contents of social media and internet platforms, and readings from sensors that track physical and environmental conditions. Some have argued that such data sets have the potential to transform our understanding of human behavior and society, constituting a meta-field known as computational social science. Criminology and criminal justice are no exception to this excitement. Although researchers in these areas have long used administrative records, in recent years they have increasingly looked to the most recent versions of these data, as well as other novel resources, to pursue new questions and tools.

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Open-Source’s Inspirations for Computational Social Science: Lessons from a Failed Analysis

Media and Communication ◽

10.17645/mac.v8i3.3163 ◽

2020 ◽

Vol 8 (3) ◽

pp. 231-238 ◽

Cited By ~ 1

Author(s):

Nathaniel Poor

Keyword(s):

Social Science ◽

Open Source ◽

Computational Social Science ◽

Digital Data ◽

Institutional Review Boards ◽

Data Sets ◽

Complex Data ◽

Institutional Review ◽

Complex Data Sets ◽

Complex Statistical Analysis

The questions we can ask currently, building on decades of research, call for advanced methods and understanding. We now have large, complex data sets that require more than complex statistical analysis to yield human answers. Yet as some researchers have pointed out, we also have challenges, especially in computational social science. In a recent project I faced several such challenges and eventually realized that the relevant issues were familiar to users of free and open-source software. I needed a team with diverse skills and knowledge to tackle methods, theories, and topics. We needed to iterate over the entire project: from the initial theories to the data to the methods to the results. We had to understand how to work when some data was freely available but other data that might benefit the research was not. More broadly, computational social scientists may need creative solutions to slippery problems, such as restrictions imposed by terms of service for sites from which we wish to gather data. Are these terms legal, are they enforced, or do our institutional review boards care? Lastly—perhaps most importantly and dauntingly—we may need to challenge laws relating to digital data and access, although so far this conflict has been rare. Can we succeed as open-source advocates have?

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