For a heterodox computational social science

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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Is Digital Scholarship Meaningful?: A Campus Study Tracking Multidisciplinary Perceptions

KULA knowledge creation dissemination and preservation studies ◽

10.18357/kula.130 ◽

2021 ◽

Vol 5 (2) ◽

Author(s):

Nickoal Eichmann-Kalwara ◽

Frederick Carey ◽

Melissa Hart Cantrell ◽

Stacy Gilbert ◽

Philip B. White ◽

...

Keyword(s):

Graduate Students ◽

Social Science ◽

Research Process ◽

Computational Social Science ◽

Local Context ◽

Research Strategies ◽

Digital History ◽

Domain Specific ◽

University Of Colorado ◽

Digital Scholarship

Increased computational and multimodal approaches to research over the past decades have enabled scholars and learners to forge creative avenues of inquiry, adopt new methodological approaches, and interrogate information in innovative ways. As such, academic libraries have begun to offer a suite of services to support these digitally inflected and data-intense research strategies. These supports, dubbed digital scholarship services in the library profession, break traditional disciplinary boundaries and highlight the methodological significance of research inquiry. Externally, however, these practices appear as domain-specific niches, e.g., digital history or digital humanities in humanities disciplines, e-science and e-research in STEM, and e-social science or computational social science in social science disciplines. The authors conducted a study examining the meaningfulness of the term digital scholarship within the local context at University of Colorado Boulder by investigating how the interpretation of digital scholarship varies according to graduate students, faculty, and other researchers. Nearly half of the definitions (46 percent) mentioned research process or methods as part of digital scholarship. Faculty and staff declined or were unable to define digital scholarship more often than graduate students or post-doctoral researchers. Therefore, digital scholarship as a term is not meaningful to all researchers. We recommend that librarians inflect their practices with the understanding that researchers and library users’ perceptions of digital scholarship vary greatly across contexts.

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Computational Social Science, Big Data, and Networks

The Oxford Handbook of Social Networks ◽

10.1093/oxfordhb/9780190251765.013.23 ◽

2020 ◽

pp. 515-534

Author(s):

Bruno Abrahao ◽

Paolo Parigi

Keyword(s):

Machine Learning ◽

Big Data ◽

Social Science ◽

Social Life ◽

Computational Models ◽

Social Space ◽

Computational Social Science ◽

Social Processes ◽

New Science ◽

Building Models

The emergence of Big data and a quantified social space has prompted the birth of a new science, computational social science (CSS), whose roots are founded in research aiming to describe social processes using computational models. Big data now fuels rapid advancements in the field, providing the basis for building models and algorithms of human behavior. New sources of massive amounts of data fundamentally reflect interactions, and, in this context, networks are intuitive abstractions to model our social life, especially that mediated by technology. The chapter introduces several examples of empirical and theoretical CSS research employing network analysis, machine learning and online experiments. It concludes with a list of challenges confronting CSS practitioners, in and outside of academia.

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Engels@200: Friedrich Engels and Digital Capitalism. How Relevant Are Engels’s Works 200 Years After His Birth?

tripleC Communication Capitalism & Critique Open Access Journal for a Global Sustainable Information Society ◽

10.31269/triplec.v19i1.1228 ◽

2020 ◽

Vol 19 (1) ◽

pp. 15-51

Author(s):

Christian Fuchs

Keyword(s):

Social Science ◽

Working Class ◽

Trade Unions ◽

Social Research ◽

Computational Social Science ◽

Digital Capitalism ◽

The Social ◽

Critical Social Science ◽

Friedrich Engels ◽

Scientific Socialism

This paper takes Friedrich Engels 200th birthday on 28 November 2020 as occasion to ask: How relevant are Friedrich Engels’s works in the age of digital capitalism? It shows that Engels class-struggle oriented theory can and should inform 21st century social science and digital social research. Based on a reading of Engels’s works, the article discusses how to think of scientific socialism as critical social science today, presents a critique of computational social science as digital positivism, engages with foundations of digital labour analysis, the analysis of the international division of digital labour, updates Engels’s Condition of the Working Class in England in the age of digital capitalism, analyses the role of trade unions and digital class struggles in digital age, analyses the social murder of workers in the COVID-19 crisis, engages with platform co-operatives, digital commons projects and public service Internet platforms are concrete digital utopias that point beyond digital capital(ism). Engels’s analysis is updated for critically analysing the digital conditions of the working class today, including the digital labour of hardware assemblers at Foxconn and Pegatron, the digital labour aristocracy of software engineers at Google, online freelance workers, platform workers at capitalist platform corporations such as Uber, Deliveroo, Fiverr, Upwork, or Freelancer, and the digital labour of Facebook users. Engels’s 200th birthday reminds us of the class character of digital capitalism and that we need critical digital social science as a new form of scientific socialism.

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Computational Social Science and Sociology

Annual Review of Sociology ◽

10.1146/annurev-soc-121919-054621 ◽

2020 ◽

Vol 46 (1) ◽

pp. 61-81

Author(s):

Achim Edelmann ◽

Tom Wolff ◽

Danielle Montagne ◽

Christopher A. Bail

Keyword(s):

Social Science ◽

Human Behavior ◽

Sociology Of Knowledge ◽

Sociological Theory ◽

Group Formation ◽

Computational Social Science ◽

Digital Data ◽

Administrative Records ◽

Historical Archives ◽

Depth Analysis

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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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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