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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.
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 eld 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 sciencecomputational 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 scientic
research have begun to cluster under the label of computa-
tional social science(CSS), an interdisciplinary subeld
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 eld has opened up exciting new research opportunities
and enabled unprecedented examination of social phenom-
ena. CSS is one of the most rapidly growing elds in aca-
demia, involving a wide range of scientic 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 aWeltanschauungfounded in a computa-
tional paradigm of society(Ciof-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
ows. 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 hardsocial science; a sociology of
the 21st centuryenabled 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
Corresponding author:
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:// which permits any use, reproduction and distribution of the work without further permission
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Original Research Article
Big Data & Society
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DOI: 10.1177/20539517211047725
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 tracesor 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 scientic paradigm
which provides tools and scientic legitimacy to a mode
of capitalist accumulation. CSS is located at the interstices
between academia and industry, as the eld provides the
training, methods, theories, and legitimacy that help instill
digital data with economic value, with many leading scho-
lars being afliated 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 elds 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 reexive(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 ag of CSS, which is emer-
ging as the foremost label for social scientic 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 eld 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 eld can be described
as part of a new paradigm the computational paradigm of
society, as Ciof-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 Ciof-Revilla draws strongly on physics and
computer science, expressed in the oft-mentioned predic-
tion that data will revolutionizethe social sciences. In
this view, Big Data will enable a hard scienceapproach
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 physicsof 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 societycom-
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 scientic
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
nally found our telescope. Let the revolution begin.
In some ways, this call goes beyond even making social
science a hardscience in the sense of replicable, cumula-
tive, and coherent(Lazer et al., 2020: 1062). Watts (2017)
proposes a solution-orientedsocial 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 Microsofts CEO would not be able to
nd a denitive 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 bettersocial 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
tunedto produce better outcomes. These scholars are
in other words not merely aiming to understand social
systems with the precision of a hardscience, 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 naturalismin 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 ComtesLe 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
Ciof-Revilla (2017: 7) argues, CSSnaturalism draws on
complexity science, with its historical roots in dynamical
system theory and early scientic 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-
worklines as the Newtonian universe. It is closer to the
kind of complex systems that typically preoccupy statistical
physicists today: avalanches and granular ows, ocks of
birds and sh, networks of interaction in neurology, cell
biology and technology. (Ball, 2012: IX)
This notion of the social world being a complex systemin
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 reexivity
and meaning-making of individuals can be bracketed, and
captures well situations where these play limited roles,
such as trafc congestions or pedestrian ows (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 xed 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 scientic analysis, digital data are a naturally
occurring by-product(Edwards et al., 2013) of social pro-
cesses, rather than something produced for scientic con-
sumption. The data culled from platforms such as
Facebook, Twitter, and Instagram are described as
imprintsor tracesof peoples 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 rawand 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-identication, simulation, and experi-
mentation. These strands overlap and interlink, but also
express internal tensions. The rst strand comprises the
identication 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-nding
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 Epsteins (2006) generative
social science:computer code, that reproduce some key
features of societies(Conte et al., 2012; see also
Ciof-Revilla, 2016). Such models view social institutions,
organizations, and behavior as emergingfrom 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 BarabásiAlbert model, which shows that
when creation of a network tie at the micro-level depends
on the nodes previous number of connections, the
outcome at the macro-level is a power-law distribution of
ties (Barabási and Albert, 1999).
Anal 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 articial
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
scientic 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 hardscientists
(Watts, 2013: 5), and digital social research is thus coming to
be approached as a form of data analytics. As the eld 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 nding
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 scientic 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 dening 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 nancialization before it, has become
a new frontier of accumulation and next step in capitalism
(Sadowski, 2019: 9). Just as nance capitalism is character-
ized by the subordination of processes of production to
nancialization, 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 thereto 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 worlds 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 dening 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
noncommodied, 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 protable extraction (Couldry and
Mejias, 2020).
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 quantied
playing social elds provided by platform infrastructures
(Dalton et al., 2016). These platforms are sufciently open-
ended to allow social interaction and individual expression
(Marres, 2017), but sufciently 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 protable outcomes(Zuboff, 2019: 8).
This logic has come to also shape the relationship
between citizens and governments, as governments
employ sophisticated methods for nudgingand directing
users through nimble forms of control (Törnberg and
Uitermark, 2020), for instance driving the emergence of
forms of predictive policingthat 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 tracesof social processes, but as valuable commodities
or a form of capital that are extracted and constructed in
ways that reect 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 biasor artifacts,
as CSS scholars might suggest, but better understood
through Foucaults 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 scientic
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 ethicsthat 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
reecting and reinforcing racism, sexism, and other hierarch-
ies of difference (e.g. Eubanks, 2018; Noble, 2018; Oneil,
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 scientic 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 revolutionbecame
the foundation not only of a scientic paradigm, but also
of an ideology, so has the complexity revolutionprovided
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 scientic
paradigms feed usefully the ideology and interests of the
capitalist forms.
As noted above, this ideological and epistemological
baggage of CSS has driven many social scientists to distance
themselves from the eld 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 inuential 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-
specic 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 internalcri-
tique: a critique that has a stake in the discipline, and which
attempts to reshape it in a constructive manner. This follows
Wylys (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 inuential 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 scientic ontol-
ogyconsistent 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 quantica-
tion on principle(Harvey and Reed, 1996: 296).
Drawing on research in critical realism, we briey 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 disciplines 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 Pentlands view of social
science as a tool for tuning societyor solving problems,
this calls for a social-scientic practice emphasizing cre-
ativity, conicts, 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 inuence 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 nds 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).
Methodological pluralist
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 simplication 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
nding 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-drivenCSS 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
ts well the pluralist approach suggested by, for example,
Nelson (2020), which starts from methods of pattern-
nding 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 conrm 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 identication of causal mechanisms are inse-
parable. Since causation in social systems occurs through
symbols and meaning, causal analysis by denition 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 actorsinterpretations while recognizing that they
are conditioned by history and embedded in context;
mental states which bring about action can be complex,
stratied, conict-ridden, and more or less available to
reection. 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
explanationof CSS is singularly focused on explaining
phenomena in terms of mechanisms in deeper strata
(Epstein, 2006); Ultimately, the goal would be to nd 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 scalethe individualto 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 retweetingon
Twitter rst 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 iterativepractice
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,
social context.
Example: social networks
We nally 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 Twitters following, mentioning,
or retweeting.
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 quantied 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
followingon 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 inuence (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-specic 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 Twittersfollowand
retweetsfunctionalities underpin specic cultures and ver-
naculars, playing part in shaping practices of inuential
content producers (Rogers, 2014; Schmidt, 2014).
The heterodox approach shares CSSemphasis on causal
mechanisms, complexity, and the obsolescence of the quan-
titativequalitative divide, but emphasizes the role of
culture and meaning, and views mechanisms as situational
and contingent. If DM focuses on local and platform-
specic culture, and CSS on universal network patterns,
HCSS aims to leverage medium-specic 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 backendnetwork
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 Twitters following functions,
this may consist of starting from CSSidentication of
uneven distribution of network inuence 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 CSSemphasis on
self-organized social structures, as well as DMs 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
eld 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 eld 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 commodication 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 scientic legitimacy
to a mode of capitalist accumulation, while naturalizing
and effacing conict, 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 scientic scholars to reject or
distance themselves from the eld, 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 eld in a constructive manner, to root out its problematic
epistemic assumptions by re-embedding the eldinanalter-
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
be critical.
We thank the editor and the reviewers for their extensive and con-
structive comments on earlier versions of this article.
Declaration of conicting interests
The authors declared no potential conicts of interest with respect
to the research, authorship, and/or publication of this article.
The authors disclosed receipt of the following nancial support for
the research, authorship, and/or publication of this article: This
work was supported by the European Unions Horizon 2020
research and innovation program (grant number 732942).
Petter Törnberg
Justus Uitermark
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... This new access to the computational tools availed by potent compute and high-dimensional algorithmic machinery have led to the misconception in some corners of CSS that tools themselves can, by and large, "solve all problems". Rather than confronting the contextual complexities that lie behind the social processes and historical conditions that generate observational data (Shaw, 2015;Törnberg & Uitermark, 2021), and that concomitantly create manifold possibilities for nonrandom missingness and meaningful noise, the computational solutionist reverts to a toolbox of heuristic algorithms and technical tricks to "clean up" the data, so that computational analysis can forge ahead frictionlessly (Agniel et al., 2018;Leonelli, 2021). At heart, this contextual sightlessness among some CSS researchers originates in scientistic attitudes that tend to naturalise and reify digital trace data (Törnberg & Uitermark, 2021), treating them as primitive and organically given units of measurement that facilitate the analytical capture of "social physics" , "the 'physics of culture'" (Manovich, 2011), or the "physics of society" (Caldarelli et al., 2018). ...
... Rather than confronting the contextual complexities that lie behind the social processes and historical conditions that generate observational data (Shaw, 2015;Törnberg & Uitermark, 2021), and that concomitantly create manifold possibilities for nonrandom missingness and meaningful noise, the computational solutionist reverts to a toolbox of heuristic algorithms and technical tricks to "clean up" the data, so that computational analysis can forge ahead frictionlessly (Agniel et al., 2018;Leonelli, 2021). At heart, this contextual sightlessness among some CSS researchers originates in scientistic attitudes that tend to naturalise and reify digital trace data (Törnberg & Uitermark, 2021), treating them as primitive and organically given units of measurement that facilitate the analytical capture of "social physics" , "the 'physics of culture'" (Manovich, 2011), or the "physics of society" (Caldarelli et al., 2018). The scientistic aspiration to discover invariant "laws of society" rests on this erroneous naturalisation of social data. ...
... These threats to the integrity of CSS research activity manifests in a cluster of potentially unseemly alignments and conflicts of interest between its own community of practice and those platforms, corporations, and public bodies who control access to the data resources and compute infrastructures upon which CSS researchers depend (Theocharis & Jungherr, 2021). First, there is the potentially unseemly alignment between the extractive motives of digital platforms, which monetise, monger, and link their vast troves of personal data and marshal inferences derived from these to classify, mould, and behaviourally nudge targeted data subjects, and the professional motivations CSS researchers who desire to gain access to as much of this kind of social big data as possible (Törnberg & Uitermark, 2021). A similar alignment can be seen between the motivations of CSS researchers to accumulate data and the security and control motivations of political bodies, which collect large amounts of personal data from the provision and administration of essential social goods and services often in the service of such motivations (Fourcade & Gordon, 2020). ...
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This chapter is concerned with setting up practical guardrails within the research activities and environments of Computational Social Science (CSS). It aims to provide CSS scholars, as well as policymakers and other stakeholders who apply CSS methods, with the critical and constructive means needed to ensure that their practices are ethical, trustworthy, and responsible. It begins by providing a taxonomy of the ethical challenges faced by researchers in the field of CSS. These are challenges related to (1) the treatment of research subjects, (2) the impacts of CSS research on affected individuals and communities, (3) the quality of CSS research and to its epistemological status, (4) research integrity, and (5) research equity. Taking these challenges as motivation for cultural transformation, it then argues for the incorporation of end-to-end habits of Responsible Research and Innovation (RRI) into CSS practices, focusing on the role that contextual considerations, anticipatory reflection, impact assessment, public engagement, and justifiable and well-documented action should play across the research lifecycle. In proposing the inclusion of habits of RRI in CSS practices, the chapter lays out several practical steps needed for ethical, trustworthy, and responsible CSS research activities. These include stakeholder engagement processes, research impact assessments, data lifecycle documentation, bias self-assessments, and transparent research reporting protocols.
... For example, using digital process data for scientific purposes requires the development of tailored data and measurement theories, quality criteria, and corresponding quality assurance procedures to establish quality standards comparable to those from survey methodology. Also, this shift in perspectives afflicts the way the obtained data are typically analyzed, raising the question of how to transfer the relevant advancements from computer science to social science methodology (Törnberg and Uitermark, 2021;Jarvis et al., 2022). ...
... There are multiple competing epistemological concepts in the discussions about CSS (e.g., Törnberg and Uitermark, 2021). While the relevance of data and the potential consequences of "big data" for the social sciences were first addressed long before societal digitization, it was the digitalization wave of the late 20th century that brought the discussion to a broader part of the scientific communities. ...
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The future viability of the empirical social sciences will depend on their ability to adapt to the conditions associated with the ongoing digitization of society. While new digital technologies have provided empirical social research with unique opportunities for data generation and analytical processing, they also impose new methodological challenges that shape research designs, theoretical foundations, and the methods used. For example, using digital process data for scientific purposes requires the development of tailored data and measurement theories, quality criteria, and corresponding quality assurance procedures to establish quality standards comparable to those from survey methodology. Also, this shift in perspectives afflicts the way the obtained data are typically analyzed, raising the question of how to transfer the relevant advancements from computer science to social science methodology. Against this backdrop, the Research Topic covers two core elements of CSS, (i) big data and (ii) machine learning. While this editorial focuses on the big picture, highlighting some key aspects in both areas without purporting to represent a comprehensive review, the research papers published in this Research Topic provide detailed insights into the unfolded content area.
... In diesem neu entstandenen Bereich werden computergestützte Methoden und sozialwissenschaftliche Forschungsdesigns auf bisher nicht untersuchte Datentypen angewandt, um Erkenntnisse über gesellschaftliche Fragen und reale Probleme zu gewinnen. CSS-Forschung nutzt Methoden und Theorien zur Analyse komplexer Systeme um entsprechende sozialwissenschaftliche Phänomene zu untersuchen (Törnberg und Uitermark 2021). In diesem Sinne untersucht die CSS, wie sich Menschen verhalten (Psychologie), wie sie in Beziehung zueinander stehen (Soziologie), wie sie mit der Umwelt interagieren (Umweltsoziologie), wie sie sich regieren (Politikwissenschaften), wie sie mit Wohlstand und materiellen Gütern umgeben (Ökonomie) und wie sie im geografischen Raum agieren (Geografie). ...
... Dank der rasanten Entwicklung von Computerwerkzeugen und der wachsenden Kapazität zur Analyse großer Datensätze ermöglicht CSS die Untersuchung sozialer Phänomene auf bislang nicht da gewesene Art und Weise (Törnberg und Uitermark 2021). Zu den in der CSS-Forschung untersuchten Themen gehören unter anderem die Analyse sozialer Netzwerke (Kitts und Quintane 2020), Medienstudien (Farrell 2019), Bevölkerungsverhalten (Godoy-Lorite und Jones 2021), das Verständnis von Kulturen und Konflikten (Hao et al. 2022) und Bevölkerungsdynamik (Woods et al. 2022. ...
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Die zunehmende Verfügbarkeit digitaler Daten und die wachsende Rechenleistung für deren Analyse eröffnen Möglichkeiten für die Analyse der sozialen Dimensionen von Umweltthemen. In diesem Kapitel geben wir einen Überblick über etablierte und neu entstehende computergestützte Methoden und Datentypen, die die umwelt-soziologische Forschung unterstützen können. Wir gehen auf Fallstudienbeispiele ein, stellen Forschungsfragen vor, die untersucht werden könnten, und diskutieren offene Herausforderungen. So wird beispielsweise erörtert, wie Webressourcen, Sensordaten und konventionelle Datentypen analysiert werden können, um neue Erkenntnisse zu gewinnen. Zu diesem Zweck werden u. a. Anwendungen wie die computergestützte Textanalyse, die Netzwerkanalyse und komplexe Systemmodellierung vorgestellt. Wir zeigen auch auf, wie neue computergestützte Methoden dazu beitragen können, Muster in den Daten zu erkennen, die mit herkömmlichen Analysemethoden möglicherweise nicht zu erkennen sind. Insgesamt bieten computergestützte sozialwissenschaftliche Methoden aufregende neue Möglichkeiten für die Forschung auf dem Gebiet der Umweltsoziologie. Durch die Bereitstellung neuer Datenanalyse- und Modellierungswerkzeuge können diese Methoden die Forschung zu einem breiten Spektrum von Umweltthemen unterstützen, vom Klimawandel und dem Management natürlicher Ressourcen bis hin zu Umweltgerechtigkeit und Nachhaltigkeitsforschung.
... The knowledge graph is a structured graphical data model for integrating the data into the domain knowledge representation and reasoning. It is generated by acquiring new knowledge by gathering information and incorporating them into a topological structure (Törnberg and Uitermark 2021). Information governance, fraud prevention, organisational learning, search, chatbots, recommendation, and intelligent systems across many organisational units are just a few of the things that knowledge graphs may help with. ...
... The customers who have utilized the resources have provided some responses based on their experience. The other new customers can use it to understand the Airbnb system based on positive and negative reactions (Törnberg and Uitermark 2021). The response is based on the system's services like transport, guidance, residential, food, management, and cleaning services. ...
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This research concentrates on extracting and context-specific discussion of Airbnb usage knowledge graphs to improve private social systems. The Knowledge-Infused Learning Techniques are applied to the learning and social impact of Airbnb usage user's system. This research Extracting and discusses Airbnb usage using knowledge graphs. This research formulates the two proposed methods for Extracting Airbnb usage knowledge graphs to improve private social systems. This research enables the two potential implications for user expectation Extraction and context-specific discussion about personal social systems. This might be useful to enhance the specific services of personal social systems. This led by using the knowledge graphs concerning the responsibilities and services using response-based Optical Character Recognition. This might be fulfilled with the internal data and explain factor for "Airbnb private systems" based on knowledge graphs and machine learning. However, the Graph convolutional networks work based on the Convolutional Neural Networks for automatically Extracting the essential features without any human supervision based on a context-specific discussion of Airbnb systems. The financial portion of the computational social system application is 45.8%, followed by the public health portion at 56.8%, the environment portion at 69.3%, the politics policy portion at 72%, the social behavior portion at 78%, the human behavior portion at 80%, and the social system portion at 85% better performance in the Airbnb usage knowledge process. The efficiency of this analysis is around 67.9%. The input data second level range is 23–39%, the improved accuracy range is 74.38%, and the increased accuracy range is 46.33%. The enhanced accuracy range is 96.5%, and the third-level input data range is 43–59%. This rough comparison result has an efficiency of 62.51%. The outcomes of several social network comparison experiments are compared to the knowledge-infused learning and classification model, and the estimated result is 73.8% efficient.
... While computational methods for analyzing textual data -such Natural Language Processing and Machine Learning -have evolved quickly in recent years, they are hard to use, often requiring deep knowledge in computational methods, as well as extensive manually coded training data. Even then, the methods often achieve only limited accuracy, as they struggle with sarcasm and irony, inferences that require contextual knowledge about the world, and key interpretive tasks such as putting oneself in the shoes of the text's author [18]. ...
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This guide introduces Large Language Models (LLM) as a highly versatile text analysis method within the social sciences. As LLMs are easy-to-use, cheap, fast, and applicable on a broad range of text analysis tasks, ranging from text annotation and classification to sentiment analysis and critical discourse analysis, many scholars believe that LLMs will transform how we do text analysis. This how-to guide is aimed at students and researchers with limited programming experience, and offers a simple introduction to how LLMs can be used for text analysis in your own research project, as well as advice on best practices. We will go through each of the steps of analyzing textual data with LLMs using Python: installing the software, setting up the API, loading the data, developing an analysis prompt, analyzing the text, and validating the results. As an illustrative example, we will use the challenging task of identifying populism in political texts, and show how LLMs move beyond the existing state-of-the-art.
... Описание этих свойств представлено ниже. Во-вторых, вычислительная социология отталкивается от идеи, что общество -сложная система с эмерджентными свойствами [1]. Кроме того, нельзя забывать о моделировании (в частности, агентном моделировании, построении искусственных обществ) как об одном из основных инструментов при работе в рамках вычислительной социологии. ...
Social psychological scholarship highlights the value of leadership for mounting effective responses to the COVID‐19 pandemic. Yet considerably less is known about whether leaders and their respective communities talk about the social crisis in the same way. Through a narrative congruence framework, we interrogate how public storylines of leaders and their communities align or misalign. Utilizing a mixed methods approach, we analyse Facebook posts and comments by Metro Manila mayors and their online followers during the early months of the COVID‐19 pandemic. We uncover a narrative incongruence between (a) leaders who perform responsibility and (b) communities that demand responsiveness . Mayors prioritize equity to give the poor more relief aid, assure efficient cash disbursement, attribute higher infection rates to sufficient testing and blame noncompliant citizens for worsening outbreaks. On the other hand, communities seek equality in relief distribution, decry ambiguous cash disbursement, criticize testing failures and fault weak quarantine protocols for crisis escalation. We conclude with pathways for meaningful engagement between leaders and communities towards effective crisis response especially in the Global South.
L’IA (intelligence artificielle) colonise de plus en plus de secteurs du quotidien. Comment prendre cela en compte dans les recherches en sciences humaines portant sur la mobilité quotidienne ? Cet article est l’occasion de s’interroger sur les cadres théorique et méthodologique à mettre en place. Nous développons la notion de « corps augmenté » et considérons les objets techniques permettant l’augmentation comme des « non humains actants » (Callon, 2006). Nous débutons par une définition, précisons les modalités d’analyse de l’augmentation du corps en matière de mobilité quotidienne et mettons en perspective l’ancienneté de cette recherche. Le cadre théorique posé, nous nous penchons sur le cadre méthodologique permettant de cerner les ressorts de l’augmentation. Nous nous intéresserons alors aux applications GPS ( Global Positioning System ) intégrant l’IA, consultables sur smartphone. Objet d’un nombre croissant de téléchargements, nous montrons par une enquête exploratoire sur quatre applications, en France, comment l’IA fait évoluer la donne. Nous revenons dans la discussion sur le cadre réflexif mis en place et sur les tendances identifiées dans les résultats.
In this paper, we empirically test whether the spread of political symbols in Twitter is due to complex contagion. We analyzed behavior consisting of editing the Twitter account name to include an icon with a yellow ribbon; a symbol that represents the demand for the release of imprisoned Catalan politicians and civil leaders. To test this hypothesis, we used a behavioral, non-reactive, relational, and dynamic dataset of a large sample of potential users. First, we show that the probability of displaying a ribbon is associated with the proportion of peers who also display it (friends that share their support for the political cause). Second, we rule out alternative explanations as simple contagion and homophily. To rule out simple contagion, we run three empirically calibrated, agent-based simulations. We use our dataset to rule out homophily. And third, we suggest that adoption cannot be interpreted as the result of a compliance mechanism or as the result of normative pressures. Instead, the most plausible micro-level generative mechanism that leads to a complex contagion pattern is a peer learning process. Our study makes several contributions to the field. We show how digital data can be effectively used to identify new explananda and test the plausibility of competing behavioral explanatory mechanisms. We also contribute to the development of the theory of complex contagions. Our study widens the set of conditions for complex contagion and the set of reasons to explain why complex contagion might occur.
Smart city strategies highlight the potential to generate new type of data through new technology, for example crowdsourced data. Based on an empirical study, we show the potentials and limits of using new data for monitoring urban sustainability and especially the Sustainable Development Goals .The latest debate on smart cities and sustainability is underpinned by the United Nations’ 2030 Agenda and their accompanying Sustainable Development Goals (SDGs), which place urban data and monitoring systems at the forefront. Therefore, there is a strong need to assess the data-driven capabilities that will help achieve the SDGs . To fill the capability gaps between existing tools and SDG indicators, new smart city data sources are now available. However, scant indicators and assessment criteria have been empirically validated. This paper identifies some of the challenges alongside the potential of using new local data in urban monitoring systems. A case study of an SDG monitoring platform implementation in a district of Berlin is examined, and the results show that the use of locale-specific, and unofficial data not only improves data availability, but it also encourages local public participation. Based on our empirical findings, we determine that the incorporation of new data for urban sustainability monitoring should be treated as a complex social process.
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As the volume of digital data is growing exponentially and computational methods are advancing rapidly, network analysis is an increasingly important analytical tool to understand social life. This paper revisits the rich history of network analysis in geography and uses insights from that history to review contemporary computational social science. Based on that analysis, we synthesize the distinctive qualities of what we term geographical network analysis. Geographical network analysis presumes that networks are situated, construed through meaning, and reflect power relations. Instead of pursuing parsimonious explanations or universal theories, geographical network analysis strives to understand how uneven networks develop across space and within place through a constant back and forth between abstraction and contextualization. Drawing on the articles in this special issue, this paper illustrates how geographical network analysis can be put to work.
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Data sharing, research ethics, and incentives must improve
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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. Expected final online publication date for the Annual Review of Sociology, Volume 46 is July 30, 2020. Please see for revised estimates.
Individuals all over the world can use Airbnb to rent an apartment in a foreign city, check Coursera to find a course on statistics, join PatientsLikeMe to exchange information about one’s disease, hail a cab using Uber, or read the news through Facebook’s Instant Articles. In The Platform Society, Van Dijck, Poell, and De Waal offer a comprehensive analysis of a connective world where platforms have penetrated the heart of societies—disrupting markets and labor relations, transforming social and civic practices, and affecting democratic processes. The Platform Society analyzes intense struggles between competing ideological systems and contesting societal actors—market, government, and civil society—asking who is or should be responsible for anchoring public values and the common good in a platform society. Public values include, of course, privacy, accuracy, safety, and security; but they also pertain to broader societal effects, such as fairness, accessibility, democratic control, and accountability. Such values are the very stakes in the struggle over the platformization of societies around the globe. The Platform Society highlights how these struggles play out in four private and public sectors: news, urban transport, health, and education. Some of these conflicts highlight local dimensions, for instance, fights over regulation between individual platforms and city councils, while others address the geopolitical level where power clashes between global markets and (supra-)national governments take place.