For a heterodox computational social
Petter Törnberg and Justus Uitermark
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 eco-
nomics does for neoliberalism. In recognition of this homology, this paper develops a critique of the complexity perspec-
tive 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 computa-
tional 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.
Computational social science, complexity, digital capitalism, surveillance capitalism, neoclassical economics
Over the past decade, digital data and methods have become
the foundation of an emerging paradigm for studying social
behavior. Computational approaches to social scientiﬁc
research have begun to cluster under the label of ‘computa-
tional social science’(CSS), an interdisciplinary subﬁeld
that ‘includes analysis of web-scale observational data,
virtual lab-style experiments, and computational modeling’
(Watts, 2013: 6). Bringing powerful new methods to bear,
this ﬁeld has opened up exciting new research opportunities
and enabled unprecedented examination of social phenom-
ena. CSS is one of the most rapidly growing ﬁelds in aca-
demia, involving a wide range of scientiﬁc disciplines as
well as strong links to the private sector, and is gaining
recognition from funding agencies, governments, and the
media (Edelmann et al., 2020).
Although still diverse and in rapid change, CSS is grad-
ually beginning to establish itself as a paradigm through a
growing number of dedicated conferences and journals as
well as textbooks and manifestos. Leading scholars often
characterize CSS as not only united by more than the use
of digital data and methods, but also by a set of epistemic
perspectives –a‘Weltanschauung’founded in a ‘computa-
tional paradigm of society’(Ciofﬁ-Revilla, 2017: 1). This
perspective draws praxeomorphically (Bauman, 2013: 56)
on the interactional structure of digital data, contrasted
with ‘rows of cases and columns of variables’(Lazer
et al., 2020: 1060) of traditional quantitative methods by
emphasizing complexity and interaction, networks, and
ﬂows. Researchers with this understanding of CSS view
the advent of digital data as an opportunity to bring to the
study of social life the rigor, scope, and certainty of the
natural sciences. To these scholars, the expansion of meta-
phors from the natural sciences appears as a promise of a
coming data-driven ‘hard’social science; a ‘sociology of
the 21st century’enabled by data giving us ‘the chance to
view society in all its complexity, through the millions of
networks of person-to-person exchanges’(Pentland, cited
in Manovich, 2011: 464). At the base of this promise is
the conviction that society is a complex system that can
be understood through theories and methods that have
been developed to understand other complex systems
Amsterdam Institute for Social Science Research, University of
Amsterdam, Amsterdam, the Netherlands
Petter Törnberg, Amsterdam Institute for Social Science Research,
University of Amsterdam, Postbus 15629, 1001 NC, Amsterdam, the
Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://
creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission
provided the original work is attributed as speciﬁed on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
Original Research Article
Big Data & Society
© The Author(s) 2021
Article reuse guidelines:
such as brains, ecosystems, or ant colonies. Not all scholars
identifying as computational social scientists operate under
this framework, but the perspective constitutes the implicit
or explicit foundation for a dominant subcurrent of CSS. It
is this mainstream that we focus on and engage with in this
The advent of digital data has, however, also separately
spawned another major literature, which takes a political
economy lens to examine the role and nature of digital
data in contemporary society. Although CSS tends to
view digital data as accidental byproducts of social life –
often referred to as ‘digital traces’or ‘footprints’–that
can be used to reconstruct interactions, this literature
views digital data as commodities or a form of capital.
Under labels such as ‘informational capitalism’(Fuchs,
2010), ‘communicative capitalism’(Dean, 2005), ‘platform
capitalism’(Srnicek, 2017), ‘digital capitalism’(Sadowski,
2019), or ‘surveillance capitalism’(Foster and McChesney,
2014; Zuboff, 2019), this literature views platforms as
designed to produce and valorize data; they are deployed
to monetize social interactions by quantifying social life
to allow prediction and manipulation (Couldry and
Mejias, 2020; Cukier and Mayer-Schoenberger, 2013).
The value instilled in data relies on (belief in) the capacity
to capture, analyze, predict, and control the social world
(Van Dijck, 2014; see also Lohr, 2015). The data are, in
other words, not an accidental trace of social interaction –
au contraire, it would be more apt to say that the sociality
is the afterthought of the extraction of digital data.
Placing CSS in light of the literature on digital capital-
ism, this paper suggests that the relation between CSS
and contemporary capitalism is homologous to the relation
between neoclassical economics and neoliberal capitalism,
in the sense that that it constitutes a scientiﬁc paradigm
which provides tools and scientiﬁc legitimacy to a mode
of capitalist accumulation. CSS is located at the interstices
between academia and industry, as the ﬁeld provides the
training, methods, theories, and legitimacy that help instill
digital data with economic value, with many leading scho-
lars being afﬁliated to companies such as Microsoft or
Google. In light of this liaison between CSS and digital cap-
italism, many scholars have distanced themselves from
CSS, instead developing alternative approaches to under-
standing the role of digital data, establishing sub-
disciplinary ﬁelds such as new media studies (Rogers,
2013), digital geography (Ash et al., 2018; Leszczynski,
2015), digital sociology (Marres, 2017), and critical data
studies (Dalton et al., 2016).
This paper attempts instead to salvage the potential of
methods and metaphors developed within CSS, by
re-embedding the ‘methodological approach of data-driven
science within a different epistemological framing that
enables social scientists to draw valuable insights from
Big Data that are situated and reﬂexive’(Kitchin, 2014:
9). In recognition of the homology between CSS and
neoclassical economics, we propose a heterodox CSS
(HCSS) that parallels the response of heterodox economics
to neoclassical economics. This results in an approach that,
similar to heterodox economics, is grounded in critical
realism, and that is critical,methodological pluralist,inter-
pretative, and explanative. It does not do away with the
concept of complexity but rather situates complexity
within a broader social ontology, premised on the convic-
tion that there are no immutable laws of social life or any
one optimal solution to social problems. The purpose of
HCSS, then, is not to identify laws or provide optimal solu-
tions but to develop conjunctural explanations, offer differ-
ent ways of perceiving and addressing issues, and to
propose qualitatively different ways of organizing social
CSS as an emerging paradigm
Although the roots of CSS can be traced back to decades of
computational experiments and explorations (e.g. Conway,
1970; Langton, 1997), its recent explosive growth has been
driven largely by the new availability of digital data. These
data have brought computer scientists, physicists, and social
scientists to coalesce under the ﬂag of CSS, which is emer-
ging as the foremost label for social scientiﬁc data analytics
(Lazer et al., 2020). Although CSS remains in some ways a
‘loosely connected intellectual community of social scien-
tists, computer scientists, statistical physicists, and others’
(Lazer et al., 2020: 1060), it is coming to formulate a dis-
tinct approach and metatheory, presented in a growing
number of introductory textbooks and manifestos
(Christakis, 2012). At the same time, the ﬁeld is establish-
ing itself institutionally through annual conferences (e.g.
IC2S2) and dedicated journals (e.g. JCSS). CSS makes
use of a toolkit of sophisticated computational methods,
including natural language processing (Hirschberg and
Manning, 2015), complex/social network analysis (Watts,
2004), machine learning (Molina and Garip, 2019), and
agent-based modeling (Squazzoni, 2012).
Some CSS scholars argue that the ﬁeld can be described
as part of a new paradigm –the ‘computational paradigm of
society’, as Ciofﬁ-Revilla (2017: 2) refers to it in his intro-
ductory CSS textbook, characterized by a distinct world-
view and epistemological perspective. The worldview
described by Ciofﬁ-Revilla draws strongly on physics and
computer science, expressed in the oft-mentioned predic-
tion that data will ‘revolutionize’the social sciences. In
this view, Big Data will enable a ‘hard science’approach
to the social world, in essence bringing it into the domain
of physics. This hope can be observed in scholars such as
Lev Manovich (2016) arguing that ‘Digital is what gave
culture the scale of physics, chemistry or neuroscience.
Now we have enough data and fast enough computers to
actually study the “physics”of culture’. The physicists
Caldarelli et al. (2018: 870) similarly argue that the
2Big Data & Society
proliferation of digital data ‘provide the opportunity to
build a “physics of society”: describing a society—com-
posed of many interacting heterogeneous entities (people,
businesses, institutions)—as a physical system’. For these
scholars, the promise of CSS is, thus, not only to contribute
new methods to traditional social science research, but
rather also to supplant it with ‘an entirely new scientiﬁc
approach for social analysis’(Conte et al., 2012: 327)
aiming to ‘uncover the laws of the society’(Conte et al.,
2012). Nicholas Christakis similarly describes CSS as ‘a
new kind of social science’(Christakis, 2012), which
answers to the crisis of the old approach of empirical sociol-
ogy (Savage and Burrows, 2007) by supplanting surveys
and interviews with data mining and simulation (Conte
et al., 2012; Lazer et al., 2009). As Watts (2011: 266)
puts it, ‘just as the invention of the telescope revolutionized
the study of the heavens, so too by rendering the unmeasur-
able measurable, the technological revolution in mobile,
Web, and Internet communications has the potential to
revolutionize our understanding of ourselves …we have
ﬁnally found our telescope. Let the revolution begin’.
In some ways, this call goes beyond even making social
science a ‘hard’science in the sense of ‘replicable, cumula-
tive, and coherent’(Lazer et al., 2020: 1062). Watts (2017)
proposes a ‘solution-oriented’social science (see also Lazer
et al., 2020: 1062), expressing frustration with the preva-
lence of a multitude of immensurable theories, and
arguing that it is indicative of the dismal state of the
social sciences that Microsoft’s CEO would not be able to
ﬁnd a deﬁnitive answer in the scholarly literature to the
question of how to optimally reorganize the corporation
(Watts, cited in Van den Berg, 2017). Pentland (2015) simi-
larly envisions a society in which massive data and new
methods allow not only deeper understanding, but also
make it possible to engineer ‘better’social systems.
Pentland points to social media platforms to show the pos-
sibilities to use social pressure to control and direct social
life, treating society as a control problem which can be
‘tuned’to produce ‘better outcomes’. These scholars are
in other words not merely aiming to understand social
systems with the precision of a ‘hard’science, but also to
treat society as a system of engineering, to be tuned and
Society as a complex system
The conceptualization of society as a complex system is
expressive of an underlying ‘naturalism’in CSS: the
belief that there is a continuity between the natural and
social world that makes it unnecessary to appeal to qualities
such as conscience, intentionality or meaning to account for
social behavior. Although social physics may at times be
presented as ‘a new science’(e.g. Pentland, 2015), it
harks back to the social physics of the 19th century, with
for instance Auguste Comte’sLe Producteur describing
social physics as a science that treats ‘social phenomena
[…] as being subject to natural and invariable laws’
(Iggers, 1959, cf. Conte et al., 2012). The naturalism of con-
temporary CSS is, however, of a different variety than that
which informed 19th-century social physics. As
Ciofﬁ-Revilla (2017: 7) argues, CSS’naturalism draws on
complexity science, with its historical roots in dynamical
system theory and early scientiﬁc computing at the Los
Alamos National Laboratory in the 1970s, and linked to
earlier traditions of cybernetics (Galison, 1997). This onto-
logical stance subscribes to naturalism but brings a funda-
mental criticism against the linearity and equilibria of
traditional quantitative approaches (Cilliers, 2002). As
Ball (2012: ix) puts it, in the complexity science perspec-
tive, the traditional conceptualization of social physics:
…remains valid but it often drew on the wrong analogies.
Society does not run along the same predictable, ‘clock-
work’lines as the Newtonian universe. It is closer to the
kind of complex systems that typically preoccupy statistical
physicists today: avalanches and granular ﬂows, ﬂocks of
birds and ﬁsh, networks of interaction in neurology, cell
biology and technology. (Ball, 2012: IX)
This notion of the social world being a ‘complex system’in
the same way as complex systems within the natural world
is a recurring notion in CSS (e.g. Conte et al., 2012; Watts,
2013: 6), providing impetus to the notion that society is
amenable to the same methods and approaches. This com-
plexity perspective stems from physics and views macro-
patterns as the emergent and aggregate outcome of micro-
interactions (Conte et al., 2012). Such an approach allows
powerful insight into phenomena in which the reﬂexivity
and meaning-making of individuals can be bracketed, and
captures well situations where these play limited roles,
such as trafﬁc congestions or pedestrian ﬂows (Andersson
et al., 2014). Many of the optimization algorithms that lay
the foundation of data analysis are designed according to
the same type of nature-inspired complex system metaphor,
including machine learning and stochastic optimization.
These algorithms are often built on simulating biological
distributed computation systems, such as ants or swarms
looking for food, neural networks, or biological evolution
(Mitchell, 2009; Wahde, 2008). In this sense, the complex-
ity perspective permeates CSS both methodologically and
The ontology of complexity aligns with the relational and
interactive nature of digital data produced by digital plat-
forms, contrasted against surveys, which are argued to slot
reality into ﬁxed categories, variables, and variances, con-
cealing its interactional elements (Conte et al., 2012; Lazer
et al., 2020). The epistemic features of digital data are,
thus, taken to represent the true characteristics of the
social world –heterogeneous, interactional, and emergent
(Conte et al., 2012). Although census data are seen as
Törnberg and Uitermark 3
produced for scientiﬁc analysis, digital data are a ‘naturally
occurring by-product’(Edwards et al., 2013) of social pro-
cesses, rather than something produced for scientiﬁc con-
sumption. The data culled from platforms such as
Facebook, Twitter, and Instagram are described as
‘imprints’or ‘traces’of people’s actual behavior or moods
(Lazer et al., 2020), informing us of what people actually
do,instead of merely what they say they do (Pentland,
This type of uncritical praise of data as ‘raw’and freely
available has calmed somewhat in recent years, as questions
of ethical issues and the role of platforms in shaping data
have become topics of discussion also within the discipline.
These are, however, not seen as fundamental epistemo-
logical issues, but rather as institutional and technical pro-
blems, to be resolved by, for instance, further
consolidating CSS as a discipline, drawing out clear guide-
lines and setting ethical rules, and establishing models for
collaboration and data sharing with the private sector (see
Lazer et al., 2020).
Patterns and mechanisms
CSS has elaborated three main approaches to studying the
social world: pattern-identiﬁcation, simulation, and experi-
mentation. These strands overlap and interlink, but also
express internal tensions. The ﬁrst strand comprises the
identiﬁcation of large-scale patterns in data. Informed by
universalities in complexity science, this is a pursuit of reg-
ularities which apply in a range of social and natural
domains, epitomized by the Feigenbaum constants in
chaos theory (Cvitanovic et al., 2005), and by scaling and
power laws (West, 2017). Such laws are often taken to
point to underlying properties of the system, indicating
the corresponding universal mechanisms at play (e.g.
Watts, 2004; West, 2017). For instance, identifying a
power-law distribution in social networks may be taken to
imply preferential attachment in the formation of network
ties (Barabási and Albert, 1999). The pattern-ﬁnding
approach is also central to more descriptive research that
provides a quantitative characterization of large data.
A second strand is the linking of macro-dynamics to
micro-mechanisms through the use of simulations and com-
putational modeling, such as agent-based models. Conte
et al. (2012) refer to these models as aimed at ‘generative
explanation’, in reference to Epstein’s (2006) ‘generative
social science’:‘computer code, that reproduce some key
features of societies’(Conte et al., 2012; see also
Ciofﬁ-Revilla, 2016). Such models view social institutions,
organizations, and behavior as ‘emerging’from individual
behavior, analogously to how the behavior of an ant
colony emerges from the interactions of individual ants.
This tends to take the form of formulating hypotheses
about individual interactions, and then using computational
methods to simulate a large number of causal steps, to see
what type of macro-dynamics these generate, thus constitut-
ing a form of computational hypo-deductive modeling. An
example is the Barabási–Albert model, which shows that
when creation of a network tie at the micro-level depends
on the node’s previous number of connections, the
outcome at the macro-level is a power-law distribution of
ties (Barabási and Albert, 1999).
Aﬁnal important strand of CSS research is large-scale
online experiments, often aimed at identifying micro-
mechanisms that produce emergent phenomena. This is epi-
tomized by the use of large-scale online experiments,
drawing from the commercial use of the so-called A/B
testing to optimize digital technologies (Huang et al.,
2018). In these experiments, researchers construct or
modify online platforms and observe how user behavior
is affected by a given treatment (Centola, 2010; Kramer
et al., 2014). This experimental approach also extends to
identifying natural experiments and examining their effect
through always-on data sources (Aral and Nicolaides,
2017; Mas and Moretti, 2009), as well as artiﬁcial
intelligence-powered methods for causal inference from
observational data, for instance by automatically identify-
ing most-similar cases in large data sets (Legewie, 2016;
Pearl, 2019). Online experiments often focus on nudging
or modifying the experience for individual users, to see
how this brings about certain macro-level consequences.
For instance, de Rijt et al. (2014) show through an online
experiment that posts with many likes receive more likes,
resulting in power-law distributions.
In summary, CSS is an emerging paradigm for social
scientiﬁc research with strong links to the data analytics
industry that methodologically brings powerful computa-
tional methods to bear within the social science domain,
and epistemologically highlights the interactional complexity
of the social world which traditional quantitative methods
have tended to bracket. With the growth of CSS, ‘the study
of social phenomena has increasingly become the province
of computer scientists, physicists, and other “hard”scientists’
(Watts, 2013: 5), and digital social research is thus coming to
be approached as a form of data analytics. As the ﬁeld estab-
lishes itself through canonical publications, manifestos, and
textbooks, mainstream CSS is developing a worldview con-
gruent with the networked and interactional nature of the
social world found in online platforms and digital data.
This has brought leading scholars to characterize the social
world as a complex system –expressing hopes of ﬁnding
universal patterns and underlying mechanisms, to develop
social science into not merely a form of physics but of engin-
eering, by learning to control and tune its underlying social
Data in digital capitalism
The advent of digital data has resulted not only in the use of
data for social scientiﬁc research, but also in a substantial
4Big Data & Society
literature examining the political economy of digital data.
This literature describes a period of capitalism in which
data have become the deﬁning commodity (e.g. Dean,
2005; Foster and McChesney, 2014; Fuchs, 2010;
Sadowski, 2020; Srnicek, 2017; Zuboff, 2019). Although
diverse, this literature shares some common conclusions:
data collection has become an important motivation for
businesses and governments (Zuboff, 2019); data are valu-
able and value-creating (Arvidsson, 2016; Srnicek, 2017);
and data systems shot through with inequities and designed
for extraction and exploitation (Andrejevic, 2014; Couldry
and Mejias, 2020; Dalton et al., 2016).
Sadowski (2019) argues that data have become a form of
capital, just like money and machinery, and that the produc-
tion of data, similar to ﬁnancialization before it, has become
‘a new frontier of accumulation and next step in capitalism’
(Sadowski, 2019: 9). Just as ﬁnance capitalism is character-
ized by the subordination of processes of production to
ﬁnancialization, so brings contemporary capitalism the sub-
ordination of production to data accumulation. Digital cap-
italism is taking shape as a political economic regime driven
by the logic of accumulation, circulation, and manipulation
of digital data.
Central to understanding the relationship between data
and capitalism is that digital data is not ‘naturally occur-
ring’, but actively extracted and inscribed in such a way
as to become susceptible to evaluation, calculation, and
intervention (Cukier and Mayer-Schoenberger, 2013).
Couldry and Mejias (2020) compare the talk of data being
‘just there’to historical colonialism and the legal doctrine
of terra nullius: the idea that land such as the territory
now known as Australia supposedly belonged to ‘no one’
and was ‘for the taking’(Couldry and Mejias, 2020).
Seen in this light, the structure of digital data is the
expression of a particular way of probing and representing
the world’s features and dynamics for the sake of manipu-
lating and monetizing of human behavior (Sadowski,
2019). Digital data have to be extracted, in a process that
reduces and abstracts, stripping context and including
only certain aspects of the world. The process of generating
data, thus, constitutes a way of exercising power over the
world, by deﬁning what counts as knowledge, who has
access to it, and how it can be processed. Thatcher et al.
(2016: 994) argue that these extractive practices ‘mirror
processes of primitive accumulation or accumulation by
dispossession that occur as capitalism colonizes previously
noncommodiﬁed, private times and places’. The value of
data lies in their power to capture, predict, and control the
social world, enabling every layer of the human experience
to become the target of proﬁtable extraction (Couldry and
Platform data can, thus, not be considered traces of pre-
existing social interaction, but should rather be seen as cap-
turing the interactions that take place on the quantiﬁed
playing social ﬁelds provided by platform infrastructures
(Dalton et al., 2016). These platforms are sufﬁciently open-
ended to allow social interaction and individual expression
(Marres, 2017), but sufﬁciently controlled to structure and
format social life in ways that render it amenable for
large-scale monitoring, data analysis, and intervention
(Couldry and Hepp, 2018). Although users are, to an
extent, free to choose how they interact with these inter-
faces, they are not free to choose the context and conditions
of this interaction; they cannot choose the menu of options
from which they make their choice (Törnberg and
Uitermark, 2020). This context is provided by the plat-
forms, acting to pursue their own goals, such as extracting
user data and maximizing platform engagement, through
algorithms designed ‘to nudge, coax, tune, and herd behav-
ior toward proﬁtable outcomes’(Zuboff, 2019: 8).
This logic has come to also shape the relationship
between citizens and governments, as governments
employ sophisticated methods for ‘nudging’and directing
users through nimble forms of control (Törnberg and
Uitermark, 2020), for instance driving the emergence of
forms of ‘predictive policing’that use methods developed
for analyzing consumer behavior to predict criminal behav-
ior (Perry, 2013). More broadly, the extraction, distribution,
and use of data are situated within an emerging political
economy that has wide-ranging implications across
society (Dalton et al., 2016; Sadowski, 2020): from cities
(Ash et al., 2018; Leszczynski, 2015) and electric infra-
structure (Levenda et al., 2015) to labor (Van Doorn,
2017) and media (Van Dijck et al., 2018).
In summary, the literature on digital capitalism suggests
that we should understand data not as ‘naturally occurring’
or ‘traces’of social processes, but as valuable commodities
or a form of capital that are extracted and constructed in
ways that reﬂect and perpetuate power inequities, while
reshaping social processes into forms that best allow their
analysis and manipulation through data analysis. This
radical shift in perspective has epistemological and political
implications for CSS.
CSS and digital capitalism
In epistemological terms, the digital capitalism literature
suggests that the complexity and relationality of digital
data is no less imposed or limiting, and no more the ‘real’
structure of the social world, than the old structure of
‘rows of cases and columns of variables’(Lazer et al.,
2020: 1060). This is not a question of ‘bias’or ‘artifacts’,
as CSS scholars might suggest, but better understood
through Foucault’s concept ‘episteme’(Foucault, 2018): a
way of imposing a certain structure on the world to make
sense of it (Couldry and Mejias, 2020). When we study
the structure of data, we are, thus, studying a structure
imposed on social reality with the aim to produce data
amenable to the same type of data analytics which CSS
employs. Just as survey data are produced for scientiﬁc
Törnberg and Uitermark 5
inquiry, so are digital data shaped by certain models in such
a way as to facilitate analysis, prediction, and control.
In political terms, if digital data are not traces of social
reality, but the product of an abstraction created by power
interests, constituting valuable commodities and means of
production within contemporary capitalism, then building
a science on its epistemic features appears not as bringing
deeper understanding into the nature and structure of
social reality, but as perpetuating and lending credence
and providing methods to the current regime of accumula-
tion (Couldry and Mejias, 2020; Van Dijck, 2014). This
is not merely a question of ‘ethics’that can be addressed
through more detailed guidelines, but an issue of CSS
being fundamentally implicated in digital capitalism, pro-
viding it tools and ideological backing. Rather than ethics
guidelines, this begs for an answer to the question ‘which
side are you on?’(Byrne and Callaghan, 2013: 176).
Leading CSS scholars such as Alex Pentland have given
their unambiguous answers through manifestos that
promote digital capitalist ideology, viewing society as an
engineering problem, and suggesting a social science that
supports supplanting political life with computation as the
foundation of governance (Zuboff, 2019).
These epistemological and political issues are interlinked,
as the episteme through which reality is perceived shapes it in
turn. This is expressed in digital data and methods often
reﬂecting and reinforcing racism, sexism, and other hierarch-
ies of difference (e.g. Eubanks, 2018; Noble, 2018; O’neil,
2016). Examples include Google suggesting pornography
for the search ‘black girls’(Noble, 2018), targeting science
and technology (STEM) job ads to men (Lambrecht and
Tucker, 2019), showing advertisements for arrest records
when searching for African-American sounding names
(Sweeney, 2013), and algorithms systematically ranking
women lower when analyzing resume (Dastin, 2018).
Although corporations are addressing these sorts of issues
through the notion of ‘biases’, the trouble runs deeper, as
the algorithms are fundamentally founded on a logic of cat-
egorization and optimization (Finn, 2017). When algorithms
are employed to optimize for the interests of corporations
and governments by categorizing customers, employees,
and citizens according to purchasing power, productivity,
credit scores, health risks, and liability, then discrimination
and inequities inevitably result.
A new neoclassicism
The role of CSS in relation to digital capitalism, thus,
appears to be homologous to the relation between neoclas-
sical economics and neoliberalism, in the sense of constitut-
ing a scientiﬁc paradigm which provides essential tools and
legitimacy to a mode of capitalist accumulation. This is not
to imply an equivalence between neoliberalism and digital
capitalism. Instead, just as neoliberal elites adopted and
instrumentalized certain ideas from neoclassical economics,
the elites of digital capitalism selectively adopt and instru-
mentalized the ideology of self-organization and complex-
ity in CSS. And just as the ‘marginal revolution’became
the foundation not only of a scientiﬁc paradigm, but also
of an ideology, so has the ‘complexity revolution’provided
the epistemology for an emerging form of capital accumu-
lation: an epistemology where the social is fundamentally
computational, suggesting that data have the potential to
fully capture –and thereby commodify –the social world
(Finn, 2017; Hayles, 2010). The particular relationships
between methodology and epistemology of the scientiﬁc
paradigms feed usefully the ideology and interests of the
As noted above, this ideological and epistemological
baggage of CSS has driven many social scientists to distance
themselves from the ﬁeld in favor of more critical
approaches. These critical approaches focus less on data ana-
lytics, and more on how the procurement, processing, and
mobilization of data emanates from and reshapes power rela-
tions, emphasizing methods such digital ethnography, poli-
tical economy, discourse analysis, and the study of
affordances and infrastructures. The digital methods (DM)
approach, with an emphasis on what Marres and Gerlitz
(2016) refer to as ‘interface methods’, has become in parti-
cular inﬂuential here, focused on examining the mutual
shaping of sociality and media technology by re-purposing
the ‘social research methods [that] are already built into
digital infrastructures, devices and practices, even if they
currently tend to serve other-than-sociological ends’
(Marres, 2017: 13), resulting in a more local and medium-
speciﬁc understanding (Rogers, 2013). This constitutes an
explicit distancing from the large-scale and quantitative
‘backend’-oriented methods of CSS and its epistemological
emphasis on underlying mechanisms and patterns.
Such a distancing is not without costs although. As Wyly
(2009: 316) suggests through his ‘strategic positivism’,
when we give up certain methods, we also ‘give up the
opportunity to shape and mobilize these constructions for
progressive purposes’. This would imply instead attempting
what Schuurman and Pratt (2002) refer to as an ‘internal’cri-
tique: a critique that has a stake in the discipline, and which
attempts to reshape it in a constructive manner. This follows
Wyly’s (2009: 310) suggestion for statistics that ‘the pre-
sumed linkages between epistemology, methodology, and
politics were never fundamental or immutable’. Rather
than an outright rejection of CSS, we, therefore, propose a
careful dissection to extract its problematic epistemic fea-
tures, re-embedding its methods and metaphors in an alter-
native framework which would allow their mobilization
for different scholarly and political aims.
A heterodox computational social science
We call for an HCSS: an attempt at reshaping CSS by
re-embedding its methods in an alternative metatheoretical
6Big Data & Society
framework. Drawing on the homology with neoclassical
economics, we turn to the lessons learned in heterodox eco-
nomics when it comes to reemploying methods in service
for a different epistemic and ideological purpose. Lawson
(2006) argues in an inﬂuential paper that the lynchpin that
unites heterodox economics is a common foundation in crit-
ical realism. With its focus on the limits of naturalism, chal-
lenge of the quantitative-qualitative divide, emphasis of
causal mechanisms, and conceptualization of social com-
plexity, this metatheory provides a powerful means of chal-
lenging the linkages between epistemology, methodology,
and politics within CSS, and reemploying its methods and
metaphors for new purposes (Archer et al., 2013;
Bhaskar, 2005; Danermark et al., 2001).
Centrally, critical realism allows the complexity which
lies at the foundation of CSS to be constructively brought
into a larger realist ontology, by drawing on an existing
strand of research which integrates complexity and critical
realism (Byrne and Callaghan, 2013; Harvey and Reed,
1996; Reed and Harvey, 1992). Reed and Harvey (1992)
argue that complexity science provides a ‘scientiﬁc ontol-
ogy’consistent with a critical realist ‘philosophical ontol-
ogy’, together forming a ‘social ontology’. As Reed and
Harvey (1992: 359) put it, such a ‘complex realist’
approach ‘treats nature and society as if they were ontologi-
cally open and historically constituted; hierarchically struc-
tured, yet interactively complex; non-reductive and
indeterminate, yet amenable to rational explanation’, thus
allowing us to ‘steer a course midway between those posi-
tivists who would use chaos theory to revivify an exhausted
scientism and those postmodernists who reject quantiﬁca-
tion on principle’(Harvey and Reed, 1996: 296).
Drawing on research in critical realism, we brieﬂy outline
what such an HCSS entails by highlighting four of its qua-
lities: critical,methodological pluralist,interpretative, and
Critical realism emphasizes that social science is an insepar-
able part of its own object of study, meaning that theory
becomes a form of practice: we change the world by under-
standing it, and we understand it by changing it (Byrne,
2002; Danermark et al., 2001). In this paper, we have
seen this play out in relation to CSS, and seen the costs
of its neglect: when CSS are studying digital data, it
studies data whose economic value and structure is
shaped by the discipline’s own models and theories.
Critical realism suggests for us to acknowledge this and
take responsibility for the consequences of our research,
that is, to decide which side we are on, and what difference
we want to make (Byrne and Callaghan, 2013). This sug-
gests employing CSS as a powerful tool for alternative poli-
tical purposes than those that it has today been made to
serve, by bringing in emancipatory goals, and aiming for
justice and equality. As Wyly (2009) argues, even
approaches born out of violence, colonial thought, and con-
tinuing oppression can provide valuable strategic bases for
mobilization and organizing to challenge oppression. Data
are necessarily built on abstractions, but when consciously
employed, abstractions can be powerful tools for progres-
sive aims (Rydin, 2007).
In contrast to Watts and Pentland’s view of social
science as a tool for ‘tuning society’or ‘solving problems’,
this calls for a social-scientiﬁc practice emphasizing cre-
ativity, conﬂicts, and negotiations. Rather than treating
society as an engineering problem for which there can be
single solutions (Andersson et al., 2014), the goal
becomes to explore different possible pathways through
simulation and experiment, both in silo and in empirical
reality. This suggests thinking of computational analysis
as a form of critique. The study of digital data and
methods can be employed to critique the way algorithms
reshape social life by embodying certain interests, and
unveil the ideology embodied in digital data, rather than
perpetuating or supporting this ideology (Noble, 2018).
Central to such critical research is to go beyond merely
describing patterns, to interrogate their limits, or criticize
the structures that generate them (Danermark et al., 2001).
As Carr (2014) puts it, ‘A statistical model of society that
ignores issues of class, that takes patterns of inﬂuence as
givens rather than as historical contingencies, will tend to
perpetuate existing social structures and dynamics’. This
calls for constantly rejecting naturalization of social phe-
nomena by revealing their contingency –that things could
be otherwise. The resulting CSS ﬁnds its lineage not in
physics, medicine, or engineering but in attempts of
imagine and construe alternative futures in and through
digitization (e.g. Medina, 2011; Pickering, 2010).
As we have shown in this paper, CSS has tended to treat
digital data and methods as granting unmediated access to
the world as it ‘really exists’. This is what Bhaskar (2013)
refers to as an ‘epistemic fallacy’: confusing the abstract
and the real by treating an abstraction as a description of
concrete entity. Moving beyond this fallacy implies
acknowledging that any modeling of a social system will
involve not only a simpliﬁcation per se, but necessarily
also an ontological mismatch between model and system
that cannot be resolved through any amount of empirical
detail (Hayles, 2008). Critical realists have taken the meth-
odological implications of this to be what Danermark et al.
(2001) call ‘critical methodological pluralism’: the sugges-
tion to move beyond methodological dichotomies –quanti-
tative versus qualitative, positivism versus hermeneutics,
universalism versus particularism –and often opting for
combinations of methods. This mode of combining must,
however, be based on ontological considerations, in
Törnberg and Uitermark 7
which we aim to identify the partial perspectives that the
data and methods reveal (Zachariadis et al., 2013).
Since ‘no data, big or small, can be interpreted without
an understanding of the process that generated them’
(Shaw, 2015: 1), rather than claiming neutrality, context
is brought in by a combination between quantitative and
qualitative perspectives, combining computational, ethno-
graphic, and statistical approaches; ‘quantitative data beg
for qualitative interrogation’(Giglietto et al., 2012: 155).
Instead of triangulation, where the purpose is validating a
ﬁnding through different methods, methodological plural-
ism serves to bring into view different dimensions of
social reality. Moving between different tools and perspec-
tives, while remaining conscious of their limitations and
biases, thus seeing a whole world by catching various
‘glimpses of reality’(Byrne, 1998). Seen in this light, simu-
lations, experiments, and models all provide valuable per-
spectives that can be further buttressed if they are
complemented with other methods. We should not expect
such an exercise to result in ever greater certainty about
immutable regularities but instead view the methods as
ways of bringing out different aspects of reality and estab-
lishing contingent patterns. An example is Hepp et al.’s
(2016) combination of qualitative interviews with network
analysis, aiming to bring out both the structure and
meaning of communication networks across different plat-
forms, showing differences among individuals and groups
in how they establish and maintain relations.
A central critical realism strategy here is retroduction –a
form of logical inference which charts a middle course
between the ‘data-driven’CSS and more traditional
research. Retroduction places data in a central role, using
guided knowledge discovery techniques to identify surpris-
ing or unanticipated observations worthy of examination
and testing (see Kitchin, 2014: 6). This enables providing
knowledge of transfactual conditions, structures, and
mechanisms that cannot be directly observed in the
domain of the empirical, thus moving from surface-level
observations to deeper causal tendencies (Danermark
et al., 2001). Retroduction as a strategy for logical inference
ﬁts well the pluralist approach suggested by, for example,
Nelson (2020), which starts from methods of pattern-
ﬁnding aimed to identify surprising observations and
produce hypotheses, to then explain these observations
via in-depth and qualitative analysis, and testing using
quantitative methods. This situates the respective strengths
of different methods within an approach that enables their
application toward identifying deeper causal tendencies.
Many CSS scholars are either implicitly or, as in the case of
Watts (e.g. 2011, 2014), explicitly critical to interpretation
within social science, arguing that it tends to conﬁrm pre-
conceptions rather than contribute knowledge. Although
Watts acknowledges that interpretation is occasionally
helpful, he stresses that it should have no role in assessing
the validity of theories (Watts, 2017; cf. Turco and
Zuckerman, 2017). For critical realists, in contrast, interpre-
tation and the identiﬁcation of causal mechanisms are inse-
parable. Since causation in social systems occurs through
symbols and meaning, causal analysis by deﬁnition involves
interpretation (Collier, 1994). Interpretations are both a pre-
condition and an aspect of the causality (Carter and New,
2004). Social life, as Paul Ricoeur wrote, has its very foun-
dation in ‘substituting signs for things’(Ricoeur, 1980:
219): that is, signs that embody interpretations (Couldry
and Hepp, 2018). Social relations are inherently meaningful:
nations, corporations, religions, or families do not exist inde-
pendently of our interpretation of them.
This points to an approach that seeks an understanding
of the actors’interpretations while recognizing that they
are conditioned by history and embedded in context;
mental states which bring about action can be complex,
stratiﬁed, conﬂict-ridden, and more or less available to
reﬂection. In short, in the social world, reasons can be
causes (Byrne, 2002; Byrne and Callaghan, 2013). To dis-
regard the role of meaning-making in the study of social life
constitutes, as Hannah Arendt said of neoclassical econom-
ics, ‘nothing less than the willful obliteration of their very
subject matter’(Arendt, 2013: 57). As Couldry and Hepp
(2018: 5) argue, ‘whatever its appearance of complexity,
even of opacity, the social world remains something access-
ible to interpretation and understanding by human actors,
indeed a structure built up, in part, through those interpreta-
tions and understandings’.
This suggests an HCSS that employs computational
methods to support, rather than supplant, interpretation.
We believe that CSS includes methods that can be powerful
for this purpose, employed as part of a larger interpretative
framework, such as Nelson (2020) ‘computational
grounded theory’. Such an interpretative approach can
also be found in cultural analysis, where scholars such as
Christopher Bail (2014) use computational methods to
access meaning in large datasets, not by ‘measuring’
meaning, but by supporting interpretation.
Both CSS and critical realism aim to explain social phe-
nomena by identifying the causal mechanisms that
produce them (Danermark et al., 2001). However, there
are important differences in how explanation is understood,
leading to radically different frameworks for how to assess
and employ CSS methods in its pursuit. The ‘generative
explanation’of CSS is singularly focused on explaining
phenomena in terms of mechanisms in deeper strata
(Epstein, 2006); ‘Ultimately, the goal would be to ﬁnd rele-
vant quantities for describing societal and sociotechnical
dynamics at a microscopic and macroscopic level and to
8Big Data & Society
connect them, similar to the way thermodynamics works,
going from the smallest scale—the individual—to the
largest —society’(Caldarelli et al., 2018). From a critical
realist perspective, such an approach provides important
insights, but describes only one form of causality, while
failing to account for the agency and meaning-making
involved in emergence in the social world (Andersson
and Törnberg, 2018) as well as the role of power and col-
lective action (Uitermark, 2015).
Critical realism suggests that rather than viewing indi-
viduals as the foundation of social systems and structures
as emergent, it is more fruitful to think of them as
mutually implicated (Bourdieu, 1979; Byrne and
Callaghan, 2013). Social mechanisms –including such
widespread mechanisms as homophily or preferential
attachment –are always shaped, facilitated, or activated
by the context in which they occur, meaning that they
are not the ground zero of social life but rather contingent
and conditional (Uitermark and Van Meeteren, 2021). As
Fuchs (2007: 27) puts it, ‘the self-organization of society is
not something that happens only blindly and unconsciously
but depends on conscious, knowledgeable agents and cre-
ative social relationships’. Some agents and relationships
are more powerful than others although and this is patently
clear for digital environments that are purposefully created
to condition users and establish patterns. Such interests and
strategies should take center stage in the analysis of digital
social life but they disappear from view when only genera-
tive explanation and upward causation is declared epistemo-
logically acceptable. It is ironic that the computational social
scientists, who are at the epicenters of digital power, embrace
an ontology that blinds them to how such epicenters come
Critical realism suggests pursuing social explanation by
tracing causal processes through the structures and elements
involved, moving back and forth between upward and
downward causation. Life in digital capitalism cannot be
fully understood without accounting for all possible
causal directions, as social emergence is in constant and
mutual interaction with the structures that constrain and
structure it. For instance, the practice of ‘retweeting’on
Twitter ﬁrst emerged spontaneously and informally
among users, was then discovered by the platform and
incorporated into its design, which in turn brought about
new emergent social dynamics as messages began spread-
ing virally in the social network (Halavais, 2014).
Similarly, social movements mobilize on social platforms
against digital capitalism, as both themselves shaped by
and aiming to reshape platform technologies (e.g. Milan,
2013). As these examples show, ‘causality, in virtue of its
transitivity, gives aid and comfort neither to the holist nor
to the individualist. The causal chain just keeps rolling
along’(Sober, 1980: 95).
There is, however, a place also for the more reductionist
methodologies of CSS within a critical realist approach. For
instance, Miller (2015: 188) calls for agent-based modeling
to be embedded within critical realism, arguing for a ‘cre-
ative, developmental, experimental, and iterative’practice
of modeling that acknowledges that the conjecturing of
mechanisms involves abduction. Through a critical realist
lens, agent-based modeling appears more as computation-
ally enhanced thought-experiments, allowing us to think
through complex causality and emergence, rather than
attempts at capturing social reality (Törnberg, 2018).
Although CSS has focused primarily on individuals, these
mechanisms play out at all levels of society. The mechan-
isms revealed through experiments and simulations are
not the foundation of social life but express the workings
of a particular, and inherently contingent and provisional,
Example: social networks
We ﬁnally focus on a brief example to clarify the distinct-
iveness of HCSS through a comparison with orthodox
CSS on one side, and DM on the other. We focus on the
example of the network representations that are central in
the backend of social media platforms, and expressed in
interface patterns such as Twitter’s following, mentioning,
CSS has seen an explosion of network research in recent
years, driven by the growing availability of the network
data employed by platforms. Most CSS research treats net-
works exclusively in graph theoretical terms (Fuhse, 2015).
This approach has proven both powerful and parsimonious,
enabling the application of sophisticated algorithms to iden-
tify and quantify structural patterns, but comes at the cost of
the abstracting away what cannot be brought into the graph
formulation, often implying the systematic disregard of cul-
tural, interpretative, and intersubjective contexts. CSS
employs the quantiﬁed output of these network algorithms
to identify what are seen as universal mechanisms and pat-
terns of human behavior: ‘we want to be able to state prin-
ciples that hold for all groups, all organizations, all
societies’(Hanneman and Riddle, 2005: 196). Studying
‘following’on Twitter, for instance, CSS may focus on
questions such as the universal laws of social relationships,
or the self-organized emergence of highly unequal power-
law distribution of network inﬂuence (Gonçalves et al.,
2011; Sadri et al., 2018).
In comparison, DM emphasizes the way technology
actively participates in the enactment of relations, seeing
following, mentioning, or retweeting as co-creating social
relations (Marres, 2017: 140). This can be studied by repur-
posing of these platform functionalities toward research
purposes (Marres, 2017: 15). DM tends to emphasize inter-
pretative and qualitative aspects in these studies, employing
numbers sparingly and primarily for illustrative purposes.
In sharp contrast to the focus on universal mechanisms’
characteristic of CSS, the result is a medium-speciﬁc and
Törnberg and Uitermark 9
local understanding, giving insights into the dynamics and
culture of a given platform (Rogers, 2013). Focusing on
Twitter, DM may focus on how Twitter’s‘follow’and
‘retweets’functionalities underpin speciﬁc cultures and ver-
naculars, playing part in shaping practices of inﬂuential
content producers (Rogers, 2014; Schmidt, 2014).
The heterodox approach shares CSS’emphasis on causal
mechanisms, complexity, and the obsolescence of the quan-
titative–qualitative divide, but emphasizes the role of
culture and meaning, and views mechanisms as situational
and contingent. If DM focuses on local and platform-
speciﬁc culture, and CSS on universal network patterns,
HCSS aims to leverage medium-speciﬁc and surface-level
observations toward the critical examination of larger
forces that structure mediatized social life –such as corpor-
ate power, capitalism, and racism (see e.g. Babic et al.,
2020). CSS, thus, repurposes the ‘backend’network
methods, employed as one of the multiple methods to a ret-
roductive approach (Buch-Hansen, 2014; Törnberg and
Törnberg, 2019), aimed at critical examination of deeper
mechanisms. Focusing on Twitter’s following functions,
this may consist of starting from CSS’identiﬁcation of
uneven distribution of network inﬂuence –but moving
from this to asking what design elements produce this dis-
tribution, what social implications it has, and whose inter-
ests it serves. This acknowledges both CSS’emphasis on
self-organized social structures, as well as DM’s focus on
the agency of technology –which together implicate the
role of platforms in designing interface to produce this par-
ticular form of self-organization (Törnberg and Uitermark,
2020): for example, network representations are ubiquitous
because their data are valuable for their capacity to identify
consumer preferences, and micro-celebrities operate in
mutualistic economic symbiosis with the platforms.
CSS has, over the past decade, emerged as one of the
fastest-growing disciplines in academia, and the dominant
ﬁeld for the study of social behavior through digital data,
located in the intersection of academia and industry.
Viewing digital data from platforms as natural occurring
by-product of digital social life, this ﬁeld brings powerful
new methods and approaches to bear by approaching
social science as a form of data analytics. A dominant
strand within CSS is coming to formulate an ontology com-
mensurate with this approach, drawing on the networked
and interactional nature of digital data to characterize
social life as a complex system –that is, a pattern which
emerges bottom-up from individual interactions. Through
this complexity lens, social phenomena appear fundamen-
tally computational in nature, making the quest for knowl-
edge a quest for computation, and the social world may be
the domain of the hard sciences.
However, the literature on digital capitalism suggests
that data are less by-products of digital social life than the
primary product which these platforms are geared to
extract. Data are valuable commodities, and their complex
and interactive structure has been imposed on the social
world to make it amenable to analysis, prediction, and
control –and therefore commodiﬁcation –through pre-
cisely the data analytical tools that CSS applies and is
part of developing. Through this lens, CSS appears as a
paradigm which provides tools and scientiﬁc legitimacy
to a mode of capitalist accumulation, while naturalizing
and effacing conﬂict, power, and meaning-making from
its subject matter. CSS is, in this sense, to digital capitalism
what neoclassical economics is to neoliberalism.
These types of epistemological and ideological issues have
brought critically oriented social scientiﬁc scholars to reject or
distance themselves from the ﬁeld, in favor for alternative
means of studying digital social life. We have instead pro-
posed that the problematic linkages between methodology,
epistemology, and politics are not fundamental or immutable,
but possible to challenge and revise. Rather than rejecting
CSS and its approach, we have attempted to engage with
the ﬁeld in a constructive manner, to root out its problematic
epistemic assumptions by re-embedding the ﬁeldinanalter-
native meta-theoretical framework, thus allowing its methods
and metaphors to be mobilized for different aims. Following
the analogy with neoclassical economics, we have suggested
an HCSS, founded in an ontology of critical realism. Through
this ontological re-embedding, we argue that the concepts and
methods of CSS can provide valuable insight, contributing to
the aim of not only modeling society, but also to question it.
This proposes a CSS in its briefest terms summarized by
Wyly (2009: 317): ‘Put simply, be careful, be modest, and
We thank the editor and the reviewers for their extensive and con-
structive comments on earlier versions of this article.
Declaration of conﬂicting interests
The authors declared no potential conﬂicts of interest with respect
to the research, authorship, and/or publication of this article.
The authors disclosed receipt of the following ﬁnancial support for
the research, authorship, and/or publication of this article: This
work was supported by the European Union’s Horizon 2020
research and innovation program (grant number 732942).
Petter Törnberg https://orcid.org/0000-0001-8722-8646
Justus Uitermark https://orcid.org/0000-0002-5274-1455
10 Big Data & Society
Andersson C and Törnberg P (2018) Wickedness and the anatomy
of complexity. Futures 95: 118–138. DOI: 10.1016/j.futures.
Andersson C, Törnberg A and Törnberg P (2014) Societal systems
–complex or worse? Futures 63: 145–157. DOI: 10.1016/j.
Andrejevic M (2014) Big Data, big questions | the Big Data divide.
International Journal of Communication 8: 17.
Aral S and Nicolaides C (2017) Exercise contagion in a global
social network. Nature Communications 8(1): 1–8.
Archer M, Bhaskar R, Collier A, et al. (2013) Critical Realism:
Essential Readings. London: Routledge.
Arendt H (2013) The Human Condition. Chicago: University of
Arvidsson A (2016) Facebook and ﬁnance: On the social logic of
the derivative. Theory, Culture and Society 33(6): 3–23.
Ash J, Kitchin R and Leszczynski A (2018) Digital turn, digital
geographies? Progress in Human Geography 42(1): 25–43.
Babic M, Garcia-Bernardo J and Heemskerk EM (2020) The rise
of transnational state capital: State-led foreign investment in
the 21st century. Review of International Political Economy
Bail CA (2014) The cultural environment: Measuring culture with
Big Data. Theory and Society 43(3-4): 465–482. http://www.
Ball P (2012) Why Society is a Complex Matter: Meeting
Twenty-First Century Challenges with a New Kind of
Science. New York: Springer Science & Business Media.
Barabási A-L and Albert R (1999) Emergence of scaling in random
networks. Science (New York, N Y) 286(5439): 509–512.
Bauman Z (2013) Liquid Modernity. Cambridge: John Wiley &
Bhaskar R (2005) The Possibility of Naturalism, A Philosophical
Critique of the Contemporary Human Sciences. London:
Routledge. DOI: 10.1111/j.1468-0149.1981.tb02719.x.
Bhaskar R (2013) A Realist Theory of Science. London:
Bourdieu P (1979) Distinction: A Social Critique of the Judgement
of Taste. London: Routledge.
Buch-Hansen H (2014) Social network analysis and critical realism.
Journal for the Theory of Social Behaviour 44(3): 306–325.
Byrne D (2002) Interpreting Quantitative Data. New York: Sage.
Byrne D and Callaghan G (2013) Complexity Theory and the
Social Sciences: The State of the Art. London: Routledge.
Byrne DS (1998) Complexity Theory and the Social Sciences: An
Introduction. New York: Psychology Press.
Caldarelli G, Wolf S and Moreno Y (2018) Physics of humans,
physics for society. Nature Physics 14(9): 870.
Carr N (2014) The limits of social engineering. MIT Technology
Review. Available from: https://www.technologyreview.com/
Carter B and New C (2004) Making Realism Work. London
and New York: Routledge, 5. DOI: 10.4324/9780203624289.
(May 24, 2016). Available at SSRN: http://dx.doi.org/10.2139/
Centola D (2010) The spread of behavior in an online social
network experiment. Science (New York, N Y) 329(5996):
Christakis NA (2012) A new kind of social science for the 21st
century. Edge. Available at: http://edge.org/conversation/a-
21st-century-change-to-social-science (accessed August 2020).
Cilliers P (2002) Complexity and Postmodernism: Understanding
Complex Systems. London: Routledge.
Ciofﬁ-Revilla C (2016) Bigger Computational Social Science:
Data, Theories, Models, and Simulations–Not Just Big Data.
(May 24, 2016). Available at SSRN: http://dx.doi.org/10.
Ciofﬁ-Revilla C (2017) Computation and social science. In:
Introduction to Computational Social Science. New York:
Springer, pp. 35–102.
Collier A (1994) Critical Realism: An Introduction to Roy
Bhaskar’s Philosophy. London: Verso.
Conte R, Gilbert N, Bonelli G, et al. (2012) Manifesto of compu-
tational social science. European Physical Journal-Special
Topics 214(214): 325–346.
Conway J (1970) The game of life. Scientiﬁc American 223(4): 4.
Couldry N and Hepp A (2018) The Mediated Construction of
Reality. Cambridge: John Wiley & Sons.
Couldry N and Mejias UA (2020) The Costs of Connection: How
Data are Colonizing Human Life and Appropriating It for
Capitalism. Oxford: Oxford University Press.
Cukier K and Mayer-Schoenberger V (2013) The rise of Big Data:
How it’s changing the way we think about the world. Foreign
Affairs (Council on Foreign Relations) 92: 28.
Cvitanovic P, Artuso R, Mainieri R, et al. (2005) Chaos: classical
and quantum. ChaosBook.org (Niels Bohr Institute,
Dalton CM, Taylor L and Thatcher J (2016) Critical data studies:
A dialog on data and space. Big Data and Society 3(1): 1–9.
Danermark B, Ekstrom M, Jakobsen L, et al. (2001) Explaining
Society: An Introduction to Critical Realism in the Social
Sciences. London: Routledge.
Dastin J (2018) Amazon scraps secret AI recruiting tool that
showed bias against women. San Francisco, CA: Reuters.
Retrieved on October 9: 2018.
Dean J (2005) Communicative capitalism: Circulation and the
foreclosure of politics. Cultural Politics 1(1): 51–74.
Edelmann A, Wolff T, Montagne D, et al. (2020) Computational
social science and sociology. Annual Review of Sociology
Edwards A, Housley W, Matthew W, et al. (2013) Digital social
research, social media and the sociological imagination:
Surrogacy, augmentation and re-orientation. International
Journal of Social Research Methodology 16(3): 245–260.
Epstein JM (2006) Generative Social Science: Studies in
Agent-Based Computational Modeling. Princeton: Princeton
Eubanks V (2018) Automating Inequality: How High-Tech Tools
Proﬁle, Police, and Punish the Poor. New York: St Martin’s
Finn E (2017) What Algorithms Want: Imagination in the Age of
Computing. New York: MIT Press.
Foster JB and McChesney RW (2014) Surveillance capitalism:
Monopoly-ﬁnance capital, the military-industrial complex,
and the digital age. Monthly Review 66(3): 1.
Foucault M (2018) The Order of Things. London: Routledge.
Törnberg and Uitermark 11
Fuchs C (2007) Internet and Society: Social Theory in the
Information Age. London: Routledge.
Fuchs C (2010) Labor in informational capitalism and on the inter-
net. The Information Society 26(3): 179–196.
Fuhse JA (2015) Theorizing social networks: The relational
sociology of and around Harrison White. International
Review of Sociology 25(1): 15–44.
Galison P (1997) Image and Logic: A Material Culture of
Microphysics. Chicago: University of Chicago Press.
Giglietto F, Rossi L and Bennato D (2012) The open laboratory:
Limits and possibilities of using Facebook, Twitter, and
YouTube as a research data source. Journal of Technology in
Human Services 30(3–4): 145–159.
Gonçalves B, Perra N and Vespignani A (2011) Modeling users’
activity on twitter networks: Validation of Dunbar’s number.
PLoS One 6(8): 1–5.
Halavais A (2014) Structure of Twitter: Social and technical. In
Weller K, Bruns A, Burgess J and Mahrt M (eds) Twitter
and Society. New York, NY: Peter Lang, pp. 29–42.
Hanneman RA and Riddle M (2005) Introduction to Social
Network Methods. Los Angeles: University of California
Harvey DL and Reed M (1996) Social science as the study of
complex systems. In Kiel LD and Elliott E (eds) Chaos
Theory in the Social Sciences. Detroit, Ann Arbor, MI:
University of Michigan Press, pp. 295–324.
Hayles NK (2008) How We Became Posthuman: Virtual Bodies in
Cybernetics, Literature, and Informatics. Riverside, CA:
University of Chicago Press.
Hayles NK (2010) My Mother was a Computer. Chicago:
University of Chicago Press.
Hepp A, Roitsch C and Berg M (2016) Investigating communica-
tion networks contextually: Qualitative network analysis as
cross-media research. MedieKultur: Journal of Media and
Communication Research 32(60): 20.
Hirschberg J and Manning CD (2015) Advances in natural language
processing. Science (New York, N Y) 349(6245): 261–266.
Huang N, Chen P, Hong Y, et al. (2018) Digital nudging for online
social sharing: Evidence from a randomized ﬁeld experiment.
In: Proceedings of the 51st Hawaii international conference
on system sciences, 2018.
Iggers GG (1959) Further remarks about early uses of the term ‘social
science’.Journal of the History of Ideas, JSTOR 20(3): 433–436.
Kitchin R (2014) Big data, new epistemologies and paradigm
shifts. Big Data and Society 1(1): 1–12.
Kramer ADI, Guillory JE and Hancock JT (2014) Experimental
evidence of massive-scale emotional contagion through
social networks. Proceedings of the National Academy of
Sciences 111(24): 8788–8790.
Lambrecht A and Tucker C (2019) Algorithmic bias? An empirical
study of apparent gender-based discrimination in the display of
stem career ads. Management Science 65(7): 2966–2981.
Langton CG (1997) Artiﬁcial Life: An Overview. Boston: MIT
Lawson T (2006) The nature of heterodox economics. Cambridge
Journal of Economics 30(4): 483–505.
Lazer D, Pentland AS, Adamic L, et al. (2009) Life in the network:
The coming age of computational social science. Science
(New York, NY) 323(5915): 721.
Lazer D, Pentland A, Watts DJ, et al. (2020) Computational social
science: Obstacles and opportunities. Science (New York, NY)
Legewie J (2016) Racial proﬁling and use of force in police stops:
How local events trigger periods of increased discrimination.
American Journal of Sociology 122(2): 379–424.
Leszczynski A (2015) Spatial media/tion. Progress in Human
Geography 39(6): 729–751.
Levenda A, Mahmoudi D and Sussman G (2015) The neoliberal
politics of ‘smart’: Electricity consumption, household moni-
toring, and the enterprise form. Canadian Journal of
Communication 40(4): 615–636.
Lohr S (2015) Data-ism: Inside the Big Data Revolution. New
York: Simon and Schuster.
Manovich L (2011) Trending: The promises and the challenges of big
social data. Debates in the Digital Humanities 2(1): 460–475.
Manovich L (2016) Online Post. Available at: https://www.
Marres N (2017) Digital Sociology: The Reinvention of Social
Research. New York: John Wiley & Sons.
Marres N and Gerlitz C (2016) Interface methods: Renegotiating
relations between digital social research, STS and sociology.
The Sociological Review 64(1): 21–46.
Mas A and Moretti E (2009) Peers at work. American Economic
Review 99(1): 112–145.
Medina E (2011) Cybernetic Revolutionaries: Technology and
Politics in Allende’s Chile. Boston: MIT Press.
Milan S (2013) Social Movements and Their Technologies: Wiring
Social Change. New York: Springer.
Miller KD (2015) Agent-based modeling and organization
studies: A critical realist perspective. Organization Studies 36(2):
Mitchell M (2009) Complexity: A Guided Tour. Oxford: Oxford
Molina M and Garip F (2019) Machine learning for sociology.
Annual Review of Sociology 45: 27–45. DOI: 10.1146/
Nelson LK (2020) Computational grounded theory: A methodolo-
gical framework. Sociological Methods and Research 49(1):
Noble SU (2018) Algorithms of Oppression: How Search Engines
Reinforce Racism. New York: NYU Press.
O’Neil C (2016) Weapons of Math Destruction: How Big Data
Increases Inequality and Threatens Democracy. New York:
Pearl J (2019) The seven tools of causal inference, with reﬂections on
machine learning. Communications of the ACM 62(3): 54–60.
Pentland A (2015) Social Physics: How Social Networks Can
Make Us Smarter. New York: Penguin.
Perry WL (2013) Predictive Policing: The Role of Crime
Forecasting in Law Enforcement Operations. Santa Monica,
CA: Rand Corporation. https://www.rand.org/pubs/research_
Pickering A (2010) The Cybernetic Brain: Sketches of Another
Future. Chicago: University of Chicago Press.
Ricoeur P (1980) Narrative time. Critical Inquiry 7(1): 169–190.
Reed M and Harvey DL (1992) The new science and the old:
Complexity and realism in the social sciences. Journal for
the Theory of Social Behaviour 22(4): 353–380.
12 Big Data & Society
Rogers R (2013) Digital Methods. Cambridge, MA: MIT Press.
Rogers R (2014) Debanalising Twitter. Twitter and Society,
New York, NY, ix–xxxviii.
Rydin Y (2007) Indicators as a governmental technology? The
lessons of community-based sustainability indicator projects.
Environment and Planning D: Society and Space 25(4):
Sadowski J (2019) When data is capital: Dataﬁcation, accumula-
tion, and extraction. Big Data and Society 6(1): 1–12.
Sadowski J (2020) Too Smart: How Digital Capitalism is
Extracting Data, Controlling Our Lives, and Taking over the
World. Boston: MIT Press.
Sadri AM, Hasan S, Ukkusuri SV, et al. (2018) Analysis of social
interaction network properties and growth on Twitter. Social
Network Analysis and Mining 8(1): 1–13.
Savage M and Burrows R (2007) The coming crisis of empirical
sociology. Sociology 41(5): 885–899.
Schmidt J-H (2014) Twitter and the rise of personal publics.
Twitter and Society. New York, NY: Peter Lang. 3–14.
Schuurman N and Pratt G (2002) Care of the subject: Feminism
and critiques of GIS. Gender, Place and Culture: A Journal
of Feminist Geography 9(3): 291–299.
Shaw R (2015) Big Data and reality. Big Data and Society 2(2):
1–4. DOI: 10.1177/2053951715608877.
Sober E (1980) Holism, individualism, and the units of selection.
In: PSA: Proceedings of the Biennial meeting of the philoso-
phy of science association, pp. 93–121.
Squazzoni F (2012) Agent-Based Computational Sociology. New
York: John Wiley & Sons.
Srnicek N (2017) Platform Capitalism. New York: John Wiley &
Sweeney L (2013) Discrimination in online ad delivery.
Communications of the ACM 56(5): 44–54.
Thatcher J, O’Sullivsan D and Mahmoudi D (2016) Data coloni-
alism through accumulation by dispossession: New metaphors
for daily data. Environment and Planning D: Society and
Space 34(6): 990–1006.
Törnberg A (2018) Abstractions on steroids: A critical realist
approach to computer simulations. Journal for the Theory of
Social Behaviour 49(1): 12194. DOI: 10.1111/jtsb.12194.
Törnberg P and Törnberg A (2019) Minding the gap between
culture and connectivity: Laying the foundations for a relational
mixed methods social network analysis. In Froehlich DE, Rehm
M and Rienties BC (eds) Mixed Methods Approaches to Social
Network Analysis.(pp.58–71). Routledge.
Törnberg P and Uitermark J (2020) Complex control and the gov-
ernmentality of digital platforms. Frontiers of Sustainable
Cities 2: 6.
Turco CJ and Zuckerman EW (2017) Verstehen for sociology:
comment on Watts. American Journal of Sociology 122(4):
Uitermark J (2015) Longing for Wikitopia: The study and politics
of self-organisation. Urban Studies 52(13): 2301–2312.
Uitermark J and van Meeteren M (2021) Geographical network
analysis. Tijdschrift voor economische en sociale geograﬁe
Van de Rijt A, Kang SM, Restivo M and Patil A (2014) Field
experiments of success-breeds-success dynamics. Proceedings
of the National Academy of Sciences 111(19): 6934-6939.
Van den Berg J (2017) Duncan Watts: “sociale wetenschappers,
stop met navelstaren.”Sociale Vraagstukken. On-line: https://
wetenschappers-stop-met-navelstaren/ (accessed 2021-09-17).
Van Dijck J (2014) Dataﬁcation, dataism and dataveillance: Big
Data between scientiﬁc paradigm and ideology. Surveillance
and Society 12(2): 197–208.
Van Dijck J, Poell T and De Waal M (2018) The Platform
Society: Public Values in a Connective World. Oxford:
Oxford University Press.
Van Doorn N (2017) Platform labor: On the gendered and racia-
lized exploitation of low-income service work in the
‘on-demand’economy. Information, Communication and
Society 20(6): 898–914.
Wahde M (2008) Biologically Inspired Optimization Methods: An
Introduction. Southampton, UK: WIT press.
Watts DJ (2004) The ‘new’science of networks. Annual Review of
Sociology 30: 243–270.
Watts DJ (2011) Everything is Obvious: Why Common Sense is
Nonsense. New York: Atlantic Books.
Watts DJ (2013) Computational social science: Exciting progress
and future directions. The Bridge on Frontiers of Engineering
Watts DJ (2014) Common sense and sociological explanations.
American Journal of Sociology 120(2): 313–351.
Watts DJ (2017) Should social science be more solution-oriented?
Nature Human Behaviour 1(1): 1–5.
West G (2017) Scale: The Universal Laws of Life and Death in
Organisms. New York: Weidenfeld & Nicolson.
Wyly E (2009) Strategic positivism. The Professional Geographer
Zachariadis M, Scott S and Barrett M (2013) Methodological
implications of critical realism for mixed-methods research.
MIS Quarterly 37(3): 855–879.
Zuboff S (2019) The Age of Surveillance Capitalism: The Fight
for a Human Future at the New Frontier of Power. New
York: Proﬁle Books.
Törnberg and Uitermark 13