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Platform Enclosure of Human Behavior and its Measurement: Using Behavioral Trace Data against Platform Episteme

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Digital trace data from giant platforms are gaining ground in academic inquiries into human behavior. This trend accompanies contestations regarding representativeness, privacy, access, and commercial origin. Complementing existing discussions and focusing on knowledge production, we draw attention to the different measurement regimes within passively captured behavioral logs from industries. Taking an institutional perspective on measurement as a management technology, we compare platforms with third party audience measurement firms. Whereas the latter measure to provide "currency" for a multi-sided advertising market, the former measure internally for their own administrative purposes (i.e., prescribing behavior through design). We demonstrate the platform giants' twofold enclosure of first the user ecology and subsequently the previously open market for user attention. With platform trace data serving as a lifeline for scholarly research, platform episteme extends itself to enclose knowledge production. We conclude by suggesting ways in which academic quantitative social sciences may resist these platform enclosures.
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Wu, A. X., & Taneja, H. (Forthcoming). Platform Enclosure of Human Behavior and its Measurement:
Using Behavioral Trace Data against Platform Episteme.
New Media & Society.
Angela Xiao Wu
Media, Culture, and Communication
New York University
Harsh Taneja
Advertising
University of Illinois at Urbana-Champaign
Abstract
Digital trace data from giant platforms are gaining ground in academic inquiries into human
behavior. This trend accompanies contestations regarding representativeness, privacy, access, and
commercial origin. Complementing existing discussions and focusing on knowledge production,
we draw attention to the different measurement regimes within passively captured behavioral logs
from industries. Taking an institutional perspective on measurement as a management technology,
we compare platforms with third party audience measurement firms. Whereas the latter measure to
provide “currency” for a multi-sided advertising market, the former measure internally for their
own administrative purposes (i.e., prescribing behavior through design). We demonstrate the
platform giants’ two-fold enclosure of first the user ecology and subsequently the previously open
market for user attention. With platform trace data serving as a lifeline for scholarly research,
platform episteme extends itself to enclose knowledge production. We conclude by suggesting
ways in which academic quantitative social sciences may resist these platform enclosures.
Keywords
Audience Measurement, Platform Episteme, Computational Social Science, Digital Trace Data,
Knowledge Production
Acknowledgment
The authors acknowledge the excellent feedback received at Data & Society’s “Contested Data”
Academic Workshop. We thank danah boyd and Dan Bouk for organizing this amazing event.
Thanks also goes to Jessa Lingel and Josh Greenberg for their incisive comments.
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Data offered by digital platforms including Facebook, Twitter, Google, Weibo, and
Wechat have created an entire ecosystem of social research. These fine grained logs of user
behavior (sometimes called “trace data,” which we use interchangeably), promise to measure
complex behaviors accurately, addressing a long-acknowledged shortcoming of self-reported
survey data (Lazer et al., 2009; Mayer-Schönberger and Cukier, 2013). Even fields such as public
opinion research, considered bastions of the survey method, are beginning to notice the value in
platform logs (Public Opinion Quarterly, 2019).
The blossom of “Big Data” social science research has spurred much skepticism (e.g.,
boyd and Crawford, 2012). Salient in the discussion are issues of data representativeness and
sampling biases, which are often linked to existing socioeconomic inequalities in the population
(e.g., Hargittai, 2020). Science and technology studies has also highlighted stages of subjective
decision-making typically hidden in the production of data by digital technology (Busch, 2016).
More recent debates have revolved around access. As the platforms become more reluctant to
provide data (Bruns, 2019) and societal concerns about privacy simmer, especially in the wake of
the Cambridge Analytica scandal, one question animates most discussion: What should social
science academics do to continue their work (King, 2011; Puschmann, 2019)? A newly proposed
“industry-academic” partnership to regulate and negotiate data and research sharing seems to
falter due to the platforms’ continued nonaction (Social Science One, 2019). Meanwhile, data
brokers such as CrowdTangle (acquired by Facebook) have streamlined easy access to select trace
data for all the interested parties.
We bring a different aspectregimes of measurementinto the ongoing conversation.
While existing discussions consider the research process from analyzing and weighting platform
data to its contested access, we take a step back further to consider how these data come about, for
what end, in order to rethink the best practices for employing platform-generated data for inferring
human behavior. The various pitfalls of repurposing platform data for academic social sciences,
we argue, result from the misalignment between platform datafication and the mainstream
epistemological norms of quantitative disciplines. Notably, our intervention takes a different
direction than the scholarship that foregrounds the expense of quantification in terms of
decontextualization, simplification, and the privileging of measurable phenomena. While fully
acknowledging insights and critiques along these lines, our goal here is to rethink the field of
quantitative social science through different regimes of measurement behind digital logs.
To illustrate this, we bring in traditional audience measurement firms such as Nielsen and
comScore, which also generate digital logs. Once supplying data that formed the basis of
advertising trade, these market research companies are now characterized as the less efficient
predecessors of social media platforms (McGuigan, 2019). Computational social scientists tend to
treat these two measurement regimes similarly since they both provide passively logged
behavioral traces for user analytics (Zhu et al., 2019). Even ethnographic researchers of journalism
refer to both as “audience analytics” without differentiation, despite that media organizations
strategize according to the exact measurement regime in use. This equation, only superficially
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reasonable, glosses over essential differences between audience measurement in traditional media
and user analytics provided by digital platforms.
Importantly, what sets predominant giant platforms apart from traditional third party firms
is not the commercialization of measurement, because both scenes are characterized by privately
owned monopolies (Napoli and Napoli, 2019). The essential difference is that, unlike long-existing
third party audience measurement firms that measure to provide “currency” for a multi-sided open
market (i.e., advertising), platform companies measure for their own administrative purposes (i.e.,
prescribing sociality with technical design). This shift entails a two-fold corporate enclosure of,
first, the user ecology itself, and second, of the open market for user attention.
In differentiating the two regimes, our mission is not to declaim one type of data as more
“objective” than the other. As van Dijck (2014) points out, to stay vigilant of the ideology of
dataism, one should refrain from invoking its usual tropes such as “precision” and “objectivity.”
Instead, we draw on the history of technology and more recently, sociology of quantification, to
investigate measurement as an institutionally constituted technology for managing events
(Espeland and Stevens, 2008; Porter, 1995a). If datafication, as per common understanding, is the
transformation of social action into quantified data (Mayer-Schönberger and Cukier, 2013), what
we examine here, to be clear, is the institutional processes that give rise and shape to datafication.
Only by taking an institutional approach to the longer history of audience measurement
can we recognize that platform datafication represents a disrupture, for the institutional dynamics
of which they are a part are qualitatively different. When employing platform behavioral data,
thoughtful social science projects should confront their measurement conditions, and if possible,
go “against the grain” in research design to foreground platform power. To illustrate such research
orientations, we draw on insights from standpoint epistemology. Finally, we hope to demonstrate
that beyond what seems an outsized focus on platform data, social scientists may alternatively turn
to behavioral trace data from third party measurement to foreground structural influences and
social collectives in theorization.
Enclosure of Behavior: Datafication for Administering the User Ecosystem
To cast platform datafication in institutional light, we first regard platform behavioral data
as essentially administrative data that platforms generate to serve their own organizational goals.
Platform analytics, we argue, is ultimately a technology that draws on users’ behavioral traces
accumulated through multilayered platform management to evaluate and enhance “product
performance.”
Big platforms’ business strategies and technological mediations of sociality are well-
researched in qualitative research and critical data studies (as early as Gillespie, 2010). Platforms
rely on in-house user analytics to constantly alter platform architectures (e.g., search query
autocompletion, result rankings, trending algorithms, personalized recommendations, social feed
curation, just to name a few) that are meant to change what users tend to search or click, what
content they are exposed to and able to choose from, and their habitual engagement with the
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platform (e.g., Ananny, 2019; Andrejevic, 2013; Eslami et al., 2016; Grind et al., 2019). Further,
platforms conduct experimental trials including A/B testing to inform their design choice
(Hindman, 2018); they aggressively deploy select data as visible metricsLikes, Views, Retweets
(also the main type of platform behavioral data for social scientists) to induce user behavior (Gehl,
2014; Grosser, 2014; Salganik et al., 2006). These social media metrics, at the same time, are inflated
by entrepreneurial users of all kinds who strive to “game the system” (Karpf, 2012; Petre et al.,
2019). In fact, the online expansion of extremist communities, political and non-political alike, is
partially attributable to the success of these attempts by the previously marginalized niche pockets
(Ananny, 2019; Gerrard, 2018).
Crucially, the continued increase in profit drives platforms to tinker with curation to
prioritize content and products whose sales brings in more revenue, and change recommendation
systems to value “time-on-platform” over user feedback, culminating in what Nick Seaver (2019)
calls “captivation metrics” (also see Karppi, 2018, for Facebook’s wide-ranging efforts to obstruct
user disconnection). Even in the case of real users “gaming” systems metrics, research shows that
platforms only intervene when the specific methods harm their own economic benefits (Petre et al.,
2019). This also explains their demonstrated reluctance to eliminate fake accounts (for they boost
their reportable user-base) (Confessore et al., 2018). From email spam to the labyrinth of social
media discourse, “humans are not producing as much of the communicative traffic as we may
think” (Brunton, 2019, p. 16). This entire literature can be productively reframed as evidence of
platform data functioning as administrative data, which are typically the documentation of
implementation of services (Penner and Dodge, 2019). In other words, the institutional condition
for platform datafication is one in which tech giants generate and employ behavioral data to
administer platform usage to maximize corporate interests.
Indeed, “that social media platforms concomitantly
measure, manipulate,
and
monetize
online behavior” is a “paradoxical premise” (van Dijck, 2014, p. 200). Quantitative social scientists
need to confront the fact that platform data are not behavior-as-it-is, not self-evident capture of the
spontaneous unfolding of human conduct in the face of the world’s social, political, and cultural
happenings. Instead, the behaviors that platform data really reflect are part of an iterative process
whereby platform governance and its user ecosystem co-evolve. In short, platform business is
about stealthily nudging behavior, or more precisely, the records of purported user behavior, in
self-serving ways; platform data, in turn, are the administrative records (and inputs) of the
feedback loop that makes up the platform’s design process aimed at prescribing user behavior.
By regarding platform behavioral data as administrative data, we may foreground the
under-recognized scenario where academic labor has inadvertently gone into administrative
research for platforms. In a recent article, Penner and Dodge (2019) highlight the benefits in using
administrative data for social science, with a focus on data from government agencies. These
include overcoming methodological individualism by understanding individuals in their social
contexts—that is, contexts where policies and other structural changes take place. Further, it “has
the benefit of focusing researchers’ attention on the measures salient to practitioners and policy-
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makers” (p. 9), who are “positioned to make decisions about practice and policy based on
researchers’ findings.” If we replace the “social context” with platform architectures and
policymakers with platform companies, it is easy to see the tendency for social science to get
assimilated into the epistemic plane of platform datafication, and for academic research based on
platform data to be readily feedable into the “iterative process of policy (read: design)
implementation” (p. 7).
After the dot.com bust, beginning with Google, followed by Facebook, the platforms
themselves started repurposing some of their own behavioral records for ad placement, a major
development in their administrative agenda (Srnicek, 2016; Zuboff, 2019). Their shift toward an
advertising-based business model effectively translates into what we term “platform enclosure of
measurement.” In the second institutional perspective, we examine behavioral trace data in the
context of the advertising market where attention is traded.
Enclosure of Measurement: Datafication for Structuring the Advertising Market
Since the advent of advertising supported business models, commercial media systems
have tried to measure user attention. Beginning with survey research and telephone coincidentals
(Beville, 1988), the measurement of user attention, a.k.a. “audience measurement,” has since the
1940s incorporated varying degrees of “passive” (i.e., unobtrusive) measurement through electronic
meters. Audience measurement quantifies audience size and profile, of which Nielsen Television
Ratings
is a popular traditional example
.
A similar example in global online audience
measurement is comScore, which provides cross-platform traffic estimates across websites and
mobile apps. Pervading across media and markets, these measures form the basis on which
advertising real estate has been valued (Balnaves et al., 2011).
As quintessential examples of what sociologists Anand and Peterson (2000) characterized
as “market information regimes,” audience measurement systems generate reports at a predictable
frequency in a “consistent” format, following a methodology agreed upon by all stakeholders
involved. Above all, these reports are produced by a “neutral” entity—that is, an entity not party to
the transactions that such information enables. For instance,
Nielsen is neither an advertiser
(buyer) nor a media owner (seller), but a third party. Owing to third party ownership, audience
measurement data are publicly available for a fee, and every subscriber gets the same information.
An advertiser wanting to advertise on NBC and ABC can use Nielsen ratings to compare the
audiences of both these networks. They can access the full schedule of who advertised where and
when. Thus they can compare their advertising with competitors’ and also correlate it with user
attention for each spot ran. With these features, the measurement regime typified by Nielsen
provides market participants a common currency around which the market comes into its own as
an institutional field (Furchtgott-Roth et al., 2007; Taneja, 2013).
In the digital advertising market, much of third party measurement has given way to big
platforms that use trace data-based analytics to monetize online user attention via advertising. In
order to advertise on Facebook and Google, the same advertiser has to use both these companies’
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own analytics’ dashboards, which provide only platform-specific data. Further, platform analytics
provide metrics only about the advertiser’s own campaign performance, but no general metrics
that reflect aggregate user attention on the platform. For example, even to paid clients, Twitter
gives at best vague descriptions about its trending tweets. The same goes with YouTube and its
trending videos. In sum, platform metrics, unlike their third party counterparts, do not qualify as a
common currency. They instead are produced in-house in a highly opaque, individualized manner,
and inevitably platform specific.
The most critical departure is that the third party companies have no administrative
investment in the metric they are producing. Their metrics serve as administrative data only for
their subscribers but not themselves. Providers such as Nielsen and comScore do not stand to gain
if people watch more television or visit particular websites. Meanwhile, the subscribers’
competing interests ensure that third party measurement stays clear of systematic manipulations.
Any attempt by comScore to elevate New York Times’ website traffic will be checked by
advertisers who also subscribe to the same data.
Figure 1: Models for monetizing attention for advertising
By contrast, Google and Facebook, which now control the majority of the global online
advertising real estate, have enclosed major portions of the previously open and hence competitive
market mechanisms, including logging, analytics, and ad placement (see Figure 1), which gives
them enormous power over other actors (Balnaves et al., 2011; Wu and Taneja, 2019: 23). While
the data science and machine learning arms wielded by tech platforms for internal management
have blossomed, the traditional market analytics sector which media outlets and advertisers
commission to examine third party data continues to decline (Tom O'Regan, personal
communication). Significantly, since the platform’s advertising division is part of its larger
business, the user analytics that function as conduits for ad placement are part and parcel of the
platform administrative data. In other words, platforms have both the capacity and incentive to
directly profit from tinkering with measurement procedures and metric formulas (and shaping user
behavior along the way).
Advertisers and publishers have routinely expressed concerns about their dependence on
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these platforms, questioning their returns on investments (Joseph, 2018). Yet at the same time they
realize that these platforms cannot be audited for advertising effectiveness because unlike
television, online media “lacks the universal metrics that allow for valid like-for-like cost
comparisons” (Joseph, 2020). The same opaque circumstances enable aggressive micro-targeting in
economically predatory ads and political ads (which may effectuate disinformation campaigns
such as those by Cambridge Analytica).
Quantitative Social Science and the Datafication of Human Behavior
In the early 2010s, pioneering critiques accentuated academia’s growing attraction to the
“Big Data mentality” cultivated within tech companies and the two sectors’ increasing
interconnections in talents and techniques (boyd and Crawford, 2012; van Dijck, 2014). Building on
these critiques, we argue that these trends are integral to a platform enclosure of knowledge
production about human behavior. Beyond the platforms’ co-optation of academic expertise and
labor, the consequences of this form of enclosure also result from academia’s ready reliance on
platform log data. To better illustrate the stakes, we contrast this platform datafication first with
the norms of quantitative social science, and then with third-party audience measurement.
Though with varying instantiations, positivism generally pushes for research as
“depersonalizing gaze that separates subject from object” (Comaroff and Comaroff, 1992: 8), which
basically advocates for an “outsider” position from the phenomena being studied (i.e, being
“objective”). Ascending after World War II with a zenith in the 1960s, the human sciences
(sociology, political science, economics, psychology, and to a lesser extent anthropology and
history) gravitated towards positivism in a rather coherent manner, largely due to the funding
structures and other institutional politics (Steinmetz, 2005). Most quantitative fields today adhere to
this epistemological orientation. By invoking this background, we are not taking the positivist
epistemology as ideal (or natural). Nor are we limiting ourselves to acknowledging its socially
constructed nature. In fact, as historians of science have richly documented, both the meanings and
the operationalizations of “objectivity” are historically contingent (e.g., Daston and Galison, 2007).
Instead, the discussion that follows rests on the fact that positivist philosophy has
functioned as the prevailing rule of inquiry according to which quantitative social science has been
envisioned, organized, and incrementally developed. As Porter writes, “objectivity, in its various
meanings, is characterized by rather what it omits,”—that is, the term is invariably about
renouncing certain solid features of subjectivity (Porter, 1995a: 85). Whether it comes to
measurement or research design, this amounts to a studious insistence on the “ethic of personal
renunciation on the part of those who construct knowledge and make decisions” (ibid). Studying
platform user behavior while maintaining the positivist commitments requires the researcher to
position herself as an outsider to this behavior, and to achieve this entails taking hold not only of
platform log data, but also methodical data about platform architectures and internal
administrations. The absence of the latter, usually the case with academics, effectively undercuts
the epistemic integrity of quantitative social science.
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At the risk of making a far-stretched analogy, it may be provocative to characterize
academic social sciences’ dependence on platform data as prone to inhabiting the platform’s
“standpoint.” Standpoint thinking is an alternative to positivism but equally committed to
empiricism. It asks one to acknowledge the social location whence one sees and investigates, while
actively accounting for this location (“standpoint”) to produce knowledge (Harding, 1993, 2005).
We may consider platform datafication as following standpoint thinking, wherein the platform
observes and takes notes, and constantly makes sense/use of these records through its
understanding of its own entrenchment and priority (a kind of “platform self-reflexivity”). But the
problem is, when the academic researcher steps in via repurposing platform log data, she risks
adopting the view that emits from the platform’s standpoint (and notably, stemming from a
dominant location that exerts power over social lives). Furthermore, while the researcher sees (a
slice of) what the platform sees, she is simultaneously kept from an adequate sense about the
platform’s inclinations and actions, which amounts to a severely constrained “standpoint
episteme.” This analogy hopefully illustrates the deep epistemic chasm between platform
behavioral data and mainstream quantitative social science.
Ignoring this chasm has concrete consequences. Readily applying pattern recognition
techniques to platform trace data, for example, tends to create woefully underrecognized analytical
oversights that effectively veil institutional and infrastructural influences, the platform’s included.
Trace data’s large volumes appear amenable to statistical techniques that enable pattern
recognition, which computational social scientists have recently labelled as “unsupervised
machine learning.” What is often forgotten is that meaningful interpretation of these patterns,
according to the logic of social science, is dependent on the researcher’s prior knowledge about the
structural conditions underlying the generation of these data. This criterion, however, usually
cannot hold for social scientists to whom the ways in which algorithms and other architectural
features govern platform user ecology remain opaque.
Consider research on political polarization for which use of social media data is rampant.
1
When users’ tweeting patterns, their followers, who they follow, and the content of their tweets is
analyzed to assess “selective exposure,” a construct rooted in the presumption about individual
agency, the study usually fails to take into account who is exposed to what content in the first
place, since Twitter closely guards the algorithm for its timeline. Similar problems arise in using
Twitter data to study the insurgent public sphere during Occupy Wall Street when due to unknown
algorithmic workings, the very term failed to Trend (see Gillespie, 2016); or using Uber’s rides data
to study commuting patterns when Uber wields its driving force with strategies such as price
surging under the name of (predicted but unverifiable) high demand (see Rosenblat and Stark,
2016); or using YouTube, or more fantastically Netflix data, to discern media preferences, when
these platforms’ entire business rests on nudging sequences of viewing (Seaver, 2019). The ready
1
Throughout this piece, we deliberately keep these examples generic enough to invite readers to reflect on the
broader literature in this area, and hence refrain from citing specific studies.
9
application of pattern recognition techniques, therefore, functions to obscure platform power.
To elucidate our present, we invoke a historical episode in academic repurposing of
industrial trace data. In the 1960s, when large volumes of data from third party audience
measurement companies became available, along with growing access to computers, scholarly
interests turned towards employing statistical techniques to uncover patterns of audience behavior.
Factor analysis was one major such technique, which simultaneously examines the correlations of
several variables together to identify highly correlated sets of variables (factors). Early research
was quick to interpret these factors as reflecting audiences’ content preferences. However, a
thorough investigation by Andrew Ehrenberg (1968), an academic statistician versed in the
collection and reporting of Nielsen data, demonstrated that these patterns reflected structural
conditions such as program schedules and people’s socially situated availability to watch rather
than their content preferences. This episode shows that contextual knowledge about measurement
and its institutional conditions, as well as other (infra)structural influences was indispensable to
detecting and interpreting viewing patterns, and to eventually theorizing audience behavior.
Both the techniques and hardware of computing have evolved tremendously since the
1960s. Compared to researchers who seized upon factor analysis to crack large behavioral datasets
from third party measurement, today’s computational social scientists know even less about the
methods or contexts underlying the generation of platform log data. No amount of statistical
sophistication or computing power can make up for the lack of this knowledge. Moreover, since
everyone lacks knowledge about what slices, or forms, of human behavior these data represent,
even critiques on the appropriateness of Big Data analysis have remained largely conceptual and
speculative.
Viewed in the same light, third party datafication comports with quantitative social science
that asserts an “outside” view to determine regularity in data for predictive generalization. First, it
represents a prime example wherein the drive for quantitative rigor has grown from attempts to
develop a strategy of impersonality to cope with multi-sided pressures (see Porter, 1995a). To
appease contrarian interests on the open advertising market, third party measurement constantly
displays efforts to provide “precise” and “objective” metrics. All subscribers have access to its
methodology documents. AC Nielsen, the founder of the Nielsen Company, has authored multiple
peer reviewed papers in top academic journals explaining the methodology of Nielsen ratings
(Nielsen, 1945). Academics working with these data are authoritative enough to constantly testify
as experts in related court hearings. Data providers have developed the tradition to work with to
improve data quality (RTI International, 2016; Milavsky, 1992). Changes in methodology are
evaluated and contested by various affected parties, together with independent non-profits and
academics, all detailed in public fora comprising trade press and academic journals (Barnes and
Thomson, 1994; Napoli, 2005; Andrews and Napoli, 2010). For example, advertisers over the
decades have forced Nielsen to move away from paper diaries to meters for measuring television
audiences in local markets.
Second, while platform logs fail the high standards of representativeness set by survey
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research, not all log data suffer from this problem. Although not strict probability samples, panels
constructed by third party companies such as Nielsen and comScore can be evaluated for bias in
representation. These companies provide full details of the baseline or establishment surveys they
conduct to enumerate the population before constructing panels of individuals who are put under
continuous measurement. The relative transparency and accountability of methodology enables
academics repurposing such data to evaluate and address likely biases, including measurement and
non-response errors. By contrast, as platforms completely internalize the datafication process, for
academics who obtain their data by either scraping or using APIs, these errors are nearly
impossible to assess. For the same reason, in contrast to its platform counterpart, third party trace
data allow meaningful application of pattern recognition techniques.
Repurposing Behavioral Trace Data against Platform Enclosure of Knowledge
Some social scientists are able to collect their own behavioral log data (Scharkow et al.,
2020). Such data collection, which typically involves installing trackers in personal digital devices,
entails huge economic costs, technical expertise, logistic and organizational prowess, as well as
ethical conundrums. As a result, “repurposing” industrial log data became a mainstream practice.
We echo Salganick (2019) in arguing that human behavior can be meaningfully studied with log
data, provided that the researcher develops a proper knowledge of what aspect of behavior these
data represent and what they exclude, and with this knowledge, refrain from extrapolating from the
data to answering research questions out of bounds. We add to these considerations by proposing
directions of research in light of the epistemological and political concerns arising from the act of
“repurposing.”.
Repurposing Platform Trace Data to Reveal Platform Power
When artists and scholars just began to rely on metrics of social media, Nancy Baym (2013)
called for attention to the potential misalignment between values they hold dear and the “economic
values” intrinsic to social media metrics. Our analysis about institutional conditions for
datafication extends Baym’s early warning. As we have suggested, a platform operates with its
own episteme by maintaining specific procedures of capture and valuation to administer behavior
through design and on-platform incentivization. It is from this “standpoint” that arose Facebook’s
in-house experiments to induce “emotional contagion” (Kramer et al., 2014), and to an extent, user
analytics and manipulation tactics wielded by companies such as Cambridge Analytica. As
manifestations of this episteme, platform data infrastructure constantly extends itself by co-opting
intellectual endeavor and potentially reconfiguring the latter’s epistemological assumptions.
This stealth expansion of platform episteme diverges further and further away from what is
considered the ideal practice of repurposing platform data, where academic social scientists,
journalists, and activists come to advance their own agendas with radically different takes on what
the data mean or what they can do, vis-a-vis corporate data scientists (also see Acker and Donovan,
2019, who use “trading zone” to describe such data practices; Galison, 1997). To escape the gravity
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of platform episteme entails careful planning and real efforts.
What might be fruitful ways of repurposing platform behavioral data that resist platform
enclosure? To do so requires highlighting the presence of platform architectures in research
design. Platforms are increasingly like highways, power grids, and undersea cables. Society
organizes its activities around these infrastructures but typically ignores their existence (Plantin et
al., 2018). Through obscuring their multifarious influences over human behavior, platforms acquire
both economic gains and sociocultural legitimacy. The term “seamless design” basically refers to
the tactic of hiding technological mediations (Eslami et al., 2016). In-platform advertising, for
example, depends on visually blending ads with “organic posts.” Against this backdrop, platform
behavioral trace data offer researchers opportunities to push back the platform’s tendency to
conceal and bring its administrative role to the fore. This requires the researcher to laboriously
steer away from the platform standpoint to those from determinate locations (e.g., users, especially
vulnerable social groups).
Examples of such studies include algorithmic auditing that conducts reverse engineering to
spot discriminatory and other unsavory practices hidden in platform curation (Sandvig et al., 2014),
critical interrogation into platform proffered datasets that effectively downplay the influences of
platform products, such as in recent disinformation campaigns (Acker and Donovan, 2019), and the
detection of bots as well as human users whose behaviors are systematically coordinated to exploit
platform features to serve larger political and economic forces, such as in the case of state-
sponsored publicity stunts in disguise (King et al., 2017). The focus on platform interference, to be
clear, is particularly meaningful as intervention in public knowledge to raise awareness of growing
platform power. By this standard, such a research focus is comparatively less urgent, perhaps, for
social scientists working with behavioral data from non-profit platforms such as Wikipedia.
Another way to resist platform enclosure of knowledge production is to involve “outside”
data sources. Platform episteme cannot easily assimilate academic queries into how the platform
user ecology relates to broader social structures, offline world happenings, or even other
platforms. Taking platform user activities as what they are and studying their relations with non-
platform factors (e.g., Bail et al., 2019) may “put the platform in perspective.” For instance,
analyzing connections between political candidates’ Facebook fan pages, rolling poll data, and the
various candidates’ background characteristics reveals the circumstances under which social
media campaigns affect electoral momentum (Tang and Lee, 2018). As discussed in the previous
section, a potential pitfall here is to readily establish platform metrics as proxies for “generic”
human behaviors, and in so doing again render the platform a neutral vehicle. For example, does
Twitterverse stand for a behavioral proxy for online conversations when Twitter gains through
instigating more conversations? Likewise, user interactions with Weibo accounts of government
agencies should not be taken as a measure for government responsiveness.
Repurposing Third Party Trace Data to Study Structures and Collectives
A different case can be made regarding behavioral log data from third party audience
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measurement, which has remained marginalized for multiple reasons. Academics from critical
social sciences and humanities have dismissed it due to its corporate origin, a criterion which, as
shown by our analysis, veils the fundamental differences in institutional dynamics that shape
datafication. Such data are also uncommon in the current “Big Data” analyses because, among
other things, dealing with it requires a set of organizational, technical, and historical knowledge
distinct from that involved in gathering data from digital platforms. But also importantly, as we
will soon discuss, analyzing third party log data entails a level of analysis higher than the
individual, which typically falls out of the comfort zone of the mainstream of established social
sciences including sociology, psychology, and communication studies.
Taking a different tack, we argue that data of this nature are particularly attuned to
identifying and investigating latent structural influences on social collectives, which has
extraordinary bearings in the current digital climate. It is however worth cautioning upfront that,
just like with platform data, academic repurposing of third party data needs to get beyond the logic
and intention with which the data are generated. For example, in scholarships such as journalism
studies, merely reporting these data to compare the popularity of news outlets remains a
descriptive exercise and has little theoretical purchase.
Unlike platform logs that are restricted to behavior on the specific platform, third party
data with people (i.e., user panels) at its heart has the first strength of being platform neutral.
ComScore for example estimates traffic to all sites that meets its minimum sample threshold for
weighting and projecting to universe estimates. Nielsen has always provided estimates for all
measurable TV channels and radio stations. Following this, as trace data with open,
comprehensive information on sampling and measurement, third party audience data can be used
to evaluate the efficacy of traditional methods that rely on a priori prompts and self-reports.
Consider media exposure, a construct common to several social and behavioral sciences. As early
as the 1980s, scholars became aware that getting at usage through surveys could be problematic, yet
surveys remain a ubiquitous method for media exposure. This was only challenged when passively
obtained behavioral data from third party measurement were employed to gauge the error in self-
reported media usage. Political scientist Markus Prior (2009), for instance, analyzed Neilsen ratings
for news programs to estimate the extent by which news viewership obtained through the
Annenberg National Election Survey was inflated. Relatedly, economists and communication
researchers have used such data to establish that internet use measured by surveys overestimated
the extent of political polarization in the US (Gentzkow and Shapiro, 2011; Webster and Ksiazek,
2012).
In fact, in the pre-digital era, third party audience metrics had long been used for theory
building in the social sciences. In one of the earliest instances, multidisciplinary teams of
communications, marketing, and statistics scholars have repurposed television viewership data
from Nielsen and its British counterpart AGB to theorize audience behavior (Barwise and
Ehrenberg, 1988). Yielding four decades of research, this tradition demonstrates that structural
factors, such as patterns of people’s availability and scheduling strategies of networks, explained
13
audience behavior much better than the content preferences that people volunteered in survey
responses (Webster and Phalen, 1997). Sociologists have also used music sales data from Billboard
and SoundScan to document changing musical tastes among Americans alongside business
strategies of major record labels (Anand and Peterson, 2000). In short, while the survey method
tends to reduce media use as stemming from individual needs and preferences alone, third party
log data allow social scientists to model the role of structural influences; this methodological shift
has ushered in a shift in theoretical discussions (Webster, 2014). Such an orientation, we argue, is
much needed today to better comprehend the shaping forces of multifarious architectures of
platform technology and the internet more broadly.
One common critique of using third party measurement for research is about the level of
analysis. Data from comScore and Nielsen are rarely available at the respondent level (Taneja,
2016). Instead, they come as aggregations of traffic to media outlets, which can be segmented
through user demographics and often attitudes. This aggregate level of analysis is prone to issues
of ecological fallacy, which results from theorizing about (individual) human behavior when the
data are available at the level of collectives.
Indeed, comparatively platform logs offer more granular individual-level behavioral
traces. But this apparent strength has a potential downside against the backdrop of platform
enclosures of user behavior and measurement. Platform administration and by extension, digital
manipulation techniques of companies like Cambridge Analytica, all revolve around individual-
level targeting. Based on in-house data and, increasingly, external data acquired via unregulated
data transactions, platforms model behavior to generate predictive analytics that helps tailor the
singular reality each individual is exposed to, in order to nudge her behavior. In other words,
individual-level user analytics, while conforming to the conventional unit of analysis of various
social sciences, are also potentially susceptible to platform enclosure. Even more STEM-oriented
scholarly communities such as the “ACM User Modeling, Adaptation and Personalization”
conference have recognized the potential pitfall and chose “Responsible Personalization” as their
2020 theme to highlight the ethical concern.
Our unfolding economic, political, and technological contexts require us to explore
alternatives to defaulting to the sovereign individual as the self-evident basic unit of social
existence. Parallel arguments have been made that, for example, champion a reconceptualization
of platforms from private providers of individualized service to public utilities that have societal
implications and thus social responsibilities (Plantin et al., 2018). Scholars also began to promote a
shift in focus from the First Amendment that protects individual speech to the networked
landscape of platform discourses, because the latter exerts more immediate influence on the
functioning of our democratic process (Ananny, 2019). We live in a world where social collectives
bear the consequences of platform power as well as weather constantly emerging infrastructural
technologies in unequal manners. Meanwhile, due to algorithmic personalization and architectural
modulation, individual experiences in the digital environment have become more fragmented and
fluid than ever. To what extent it remains essential that theorization needs to hinge on the
14
individual? Raising the level of analysis, then, to that of collectives and creatively examining trace
data by varying aggregates can be a fruitful direction for social scientific discovery and theory
building (for such an example, see Taneja and Wu, 2014, which uses comScore global web usage
data to access the impact of stage-sponsored access blockage).
Finally, in addition to opening up new realms in empirical and theoretical research, third-
party audience measurement allows better protection of individual privacy. As discussed above, its
datafication amounts to “a form of surveillance known,” wherein with informed consent and
compensation, recruited panelists have full knowledge of how their media behaviors are being
monitored. This sharply contrasts the platforms’ often obfuscated surveillance and monetization
practices (also see Lingel, 2019).
Conclusion
Over the last decade, “Big Data” obtained from giant platforms became a fertile ground,
and for specific areas, a lifeline for academic inquiries into human behavior. In response to this
new configuration of scholarly production, from technical to ethical, concerns have been raised
regarding representativeness, data access and privacy. On the one hand, some researchers seek to
highlight the strengths of traditional methods (e.g., surveys and experiments) to the fast-growing
computational social science community where talents from STEM fields also coalesced. On the
other hand, many scholars strive to envision more accountable access and sharing protocols for
platform data. To complement these current conversations, we pursue a different line of
interrogation, which neither guards academics from corporate platform data, nor distinguishes
“Big Data” from data collected through traditional methods. Instead, we draw a line within the
general category of Big Data, typified by passively captured behavioral logs with industrial origin.
Within this kind of data, there are fundamental differences in the measurement regime that
have been elided in the existing discussion. And in here, we argue, lies the key to evaluate afresh
the now extensive efforts to employ platform log data for knowledge production. To illustrate
these consequential features of platform datafication, we introduce third-party log data with
historical, organizational, and technical specifics. Our analysis demonstrates that, arising from
different institutional conditions, regimes of behavioral measurement differ, and they lead to
different forms of knowledge via scholarly practice.
As historians of science and technology have illustrated, the “data explosion” during the
mid-20th century resulted from the expansion of public domains, which ushered in a proliferation
of “constraining” measurements and “impersonal” information (Heyck, 2015; Porter, 1995b). Our
analysis of the institutional conditions for measurement and datafication suggests that the present-
day “data explosion” represented by the prevalence of platform trace data is not an acceleration of
the existing trend but rather a reversionplatform enclosures of sociality, the public sphere, and
the evaluation of public attention with political, economic, and cultural bearings.
The divergence of platform trace data in measurement regimes is particularly
consequential, when viewed from the perspective of quantitative social science, a dominant
15
enterprise that shapes our understanding of the social world. Constituted in its distinct conditions
of datafication, platform episteme manifests in individual-level predictive analytics, the obscurity
of architectural nudges, as well as the perceptual centrality of platform-based ecology. This
episteme powerfully extends itself via the increasingly encompassing platform data
infrastructures. Against this backdrop, academic social sciences should push back by revisiting
fundamental issues about knowledge production from datafication to analysis and interpretation.
Third party measurement, in this sense, amounts to a viable and potentially countervailing source
in resistance to the enclosure of platform episteme. Ultimately, we hope this essay will be an initial
call that addresses all the stakeholdersjournalists, NGOs, and the general publicwho are
enamored by the explosion of platform Big Data and drawn to its episteme to represent and
understand our social reality and ourselves.
16
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