Digitised children’s bodies

This is an excerpt from the pre-print version of a chapter I have written on the topic of ‘digital bodies’. The full pre-print can be accessed here.

The sociomaterialist perspective has been taken up by several scholars writing about children’s bodies, particularly within cultural geography, but also by some sociologists and anthropologists (Prout, 1996; Horton and Kraftl, 2006a, 2006b; Lee, 2008; Woodyer, 2008). Researchers using a sociomaterialist approach have conducted studies on, for example, children’s use of asthma medication (Prout, 1996), the surveillant technologies that have developed around controlling children’s body weight in schools (Rich et al., 2011), children’s sleep and the objects with which they interact (Lee, 2008), the interrelationship of objects with pedagogy and classroom management of students’ bodies (Mulcahy, 2012) and sociomaterial practices in classrooms that lead to the inclusion or exclusion of children with disabilities (Söderström, 2014). Outside sociomaterialist studies, young children’s interactions with digital technologies have attracted extensive attention from social researchers, particularly in relation to topics such as the potential for cyber-bullying, online paedophilia and for children to become unfit and overweight due to spending too much time in front of screens (Holloway et al., 2013). However few researchers thus far have directed their attention to the types of digital technologies that visually represent children’s bodies or render their body functions, activities and behaviours into digital data; or, in other words, how children’s bodies become digital data assemblages.

From the embryonic stage of development onwards, children’s bodies are now routinely monitored and portrayed using digital technologies. A plethora of websites provide images of every stage of embryonic and foetal development, from fertilisation to birth, using a combination of digital images taken from embryo and foetus specimens and digital imaging software  (Lupton, 2013). 3/4D ultrasounds have become commodified, used for ‘social’ or ‘bonding’ purposes. Many companies offering 3/D ultrasounds now come to people’s homes, allowing expectant parents to invite family and friends and turn a viewing of the foetus into a party event. This sometimes involves a ‘gender reveal’ moment, in which the sonographer demonstrates to all participants, including the parents, the sex of the foetus . Some companies offer the service of using 3D ultrasound scan files to create life-sized printed foetus replica models for parents.

The posting to social media sites such as Facebook, Twitter, Instagram and YouTube of the foetus ultrasound image has become a rite of passage for many new parents and often a way of announcing the pregnancy. Using widgets such as ‘Baby Gaga’, expectant parents can upload regular status updates to their social media feeds automatically that provide news on the foetus’s development. While a woman is pregnant, she can use a range of digital devices to monitor her foetus. Hundreds of pregnancy apps are currently on the market, including not only those that provide information but others that invite users to upload personal information about their bodies and the development of their foetus. Some apps offer a personalised foetal development overview or provide the opportunity for the woman to record the size of her pregnant abdomen week by week, eventually creating a time-lapse video. Other apps involve women tracking foetal movements or heart beat. Bella Beat, for example, is a smartphone attachment and app that allows the pregnant women to hear and record the foetal heart beat whenever she likes and to upload the audio file to her social media accounts.

YouTube has become a predominant medium for the representation of the unborn entity in the form of ultrasound images and of the moment of birth. Almost 100,000 videos showing live childbirth, including both vaginal and Caesarean births, are available for viewing on that site, allowing the entry into the world of these infants to be viewed by thousands and, in the case of some popular videos, even millions of viewers. Some women even choose to live-stream the birth so that audiences can watch the delivery in real time. Following the birth, there are similar opportunities for proud parents to share images of their infant online on social media platforms. In addition to these are the growing number of devices on the market for parents to monitor the health, development and wellbeing of their infants and young children. Apps are available to monitor such aspects as infants’ feeding and sleeping patterns, their weight and height and their development and achievements towards milestones. Sensor-embedded baby clothing, wrist or ankle bands and toys can be purchased that monitor infants’ heart rate, body temperature and breathing, producing data that are transmitted to the parents’ devices. Smartphones can be turned into baby monitors with the use of apps that record the sound levels of the infant.

As children grow, their geolocation, educational progress and physical fitness can be tracked by their parents using apps, other software and wearable devices. As children themselves begin to use digital technologies for their own purposes, they start to configure their own digital assemblages that represent and track their bodies. With the advent of touchscreen mobile devices such as smartphones and tablet computers, even very young children are now able to use social media sites and the thousands of apps that have been designed especially for their use (Holloway et al., 2013). Some such technologies encourage young children to learn about the anatomy of human bodies or about nutrition, exercise and physical fitness, calculate their body mass index, collect information about their bodies or represent their bodies in certain ways (such as manipulating photographic images of themselves). These technologies typically employ gamification strategies to provide interest and motivation for use. Some involve combining competition or games with self-tracking using wearable devices. One example is the Leapfrog Leapband, a digital wristband connected to an app which encourages children to be physically active in return for providing them with the opportunity to care for virtual pets. Another is the Sqord interactive online platform with associated digital wristband and app. Children who sign up can make an avatar of themselves and use the wristband to track their physical activity. Users compete with other users by gaining points for moving their bodies as often and as fast as possible.

In the formal educational system there are still more opportunities for children’s bodies to be monitored measured and evaluated and rendered into digitised assemblages. Programmable ‘smart schools’ are becoming viewed as part of the ‘smart city’, an urban environment in which sensors that can watch and collect digital data on citizens are ubiquitous (Williamson, 2014). The monitoring of children’s educational progress and outcomes using software is now routinely undertaken in many schools, as are their movements around the school. In countries such as the USA and the UK, the majority of schools have CCTV cameras that track students, and many use biometric tracking technologies such as RFID chips in badges or school uniforms and fingerprints to identify children and monitor their movements and their purchases at school canteens (Taylor, 2013; Selwyn, 2014). A growing number of schools are beginning to use wearable devices, apps and other software for health and physical education lessons, such as coaching apps that record children’s sporting performances and digital heart rate monitors that track their physical exertions (Lupton, 2015).

We can see in the use of digital technologies to monitor and represent the bodies of children a range of forms of embodiment. Digitised data assemblages of children’s bodies are generated from before birth via a combination of devices that seek to achieve medical- or health-related or social and affective objectives. These assemblages may move between different domains: when, for example, a digitised ultrasound image that was generated for medical purposes becomes repurposed by expectant parents as a social media artefact, a way of announcing the pregnancy, establishing their foetus as new person and establishing its social relationships. Parents’ digital devices, and later those of educational institutions and those of children themselves when they begin to use digital devices, potentially become personalised repositories for a vast amount of unique digital assemblages on the individual child, from images of them to descriptions of their growth, development, mental and physical health and wellbeing, movements in space, achievements and learning outcomes. These data assemblages, containing as they do granular details about children, offer unprecedented potential to configure knowledges about individual children and also large groups of children (as represented in aggregated big data sets).

References

Holloway D, Green L and Livingstone S. (2013) Zero to Eight: Young Children and Their Internet Use. London: LSE London, EU Kids Online.

Horton J and Kraftl P. (2006a) Not just growing up, but going on: Materials, spacings, bodies, situations. Children’s Geographies 4(3): 259-276.

Horton J and Kraftl P. (2006b) What else? some more ways of thinking and doing ‘Children’s Geographies’. Children’s Geographies 4(1): 69-95.

Lee N. (2008) Awake, asleep, adult, child: An a-humanist account of persons. Body & Society 14(4): 57-74.

Lupton D. (2013) The Social Worlds of the Unborn, Houndmills: Palgrave Macmillan.

Lupton D. (2015) Data assemblages, sentient schools and digitised health and physical education (response to Gard). Sport, Education and Society 20(1): 122-132.

Mulcahy D. (2012) Affective assemblages: body matters in the pedagogic practices of contemporary school classrooms. Pedagogy, culture and society 20(1): 9-27.

Prout A. (1996) Actor-network theory, technology and medical sociology: an illustrative analysis of the metered dose inhaler. Sociology of Health and Illness 18(2): 198-219.

Rich E, Evans J and De Pian L. (2011) Children’s bodies, surveillance and the obesity crisis. In: Rich E, Monaghan LF and Aphramor L (eds) Debating Obesity: Critical Perspectives. Houndsmills: Palgrave Macmillan, 139-163.

Selwyn N. (2014) Data entry: towards the critical study of digital data and education. Learning, Media and Technology: 1-19.

Söderström S. (2014) Socio-material practices in classrooms that lead to the social participation or social isolation of disabled pupils. Scandinavian Journal of Disability Research online first.

Taylor E. (2013) Surveillance Schools: Security, Discipline and Control in Contemporary Education, Houndmills: Palgrave Macmillan.

Williamson B. (2014) Smart schools in sentient cities. dmlcentral.

Woodyer T. (2008) The body as research tool: embodied practice and children’s geographies. Children’s Geographies 6(4): 349-362.

‘Eating’ digital data

My previous post drew on Donna Haraway’s concept of companion species to theorise the ways in which we engage with our personal digital data assemblages. The work of Annemarie Mol offers an additional conceptual framework within which to understand digital data practices at a more detailed level, while still retaining the companion species perspective.

Mol has developed a framework that incorporates elements of enquiry that can be mapped onto the topic of digital data practices. These include the following: understanding language/discourse and its context and effects; tracing the development and use of objects of knowledge as they become objects-in-practice; acknowledging the dynamic nature of processes and the ‘endless tinkering’ that is involved in processes; incorporating awareness of the topologies or sites and spaces in which phenomena are generated and used; and finally, directing attention at the lived experiences or engagements in which practices and objects are understood and employed (see also Mol, 2002, 2008; Mol and Law, 2004).

If this approach is applied to digital data practices and their configurations, then focusing attention on the language that is employed to describe digital data, viewing digital data as objects that have both discursive and material effects and that constantly changing, recognising the process of tinkering (experimenting, adapting) that occur in relation to digital data and the spaces in which these processes take place are all important to developing an understanding of the ontology of digital data and our relationship with them.

Mol’s (2002) concept of ‘the body multiple’ in medicine has resonances with the Haraway’s cyborg ontology. This concept recognises that the human body is comprised of many different practices, sites and knowledges. While the body itself is not fragmented or multiple, the phenomena that make sense of it and represent it do so in many different ways so that the body is lived and experienced in different modes. So too the digital data assemblages that are configured by human users’ interactions with digital technologies are different versions of people’s identities and bodies that have material effects on their ways of living and conceptualising themselves. Part of the work of people’s data practices is negotiating the multiple bodies and selves that these digital data assemblages represent and configure.

Mol’s writings on human subjectivity also have implications for understanding data practices and interpretations. In her essay entitled ‘I eat an apple’, Mol points out that once a foodstuff has been swallowed, the human subject loses control over what happens to the content of the food in her body as the processes of digestion take place. As she notes, the body is busily responding to the food, but the individual herself has no control over this: ‘Her actorship is distributed and her boundaries are neither firm nor fixed’ (Mol, 2008: 40). The eating subject is able to choose what food she decides to eat, but after this point, her body decides how to deal with the components of the food, selecting certain elements and discarding others.

This raises questions about human agency and subjectivity. In the statement ‘I eat an apple’ is the agency in the ‘I’ or in the apple? Humans may grow, harvest and eat apples, but without foodstuffs such as apples, humans would not exist. Furthermore, once the apple is chewed and swallowed, it then becomes part of and absorbed into the eater’s body. It is impossible to determine what is human and what is apple (Mol, 2008: 30) The eating subject, therefore, is semi-permeable, neither completely closed off nor completely open to the world.

Mol then goes on to query at what stage the apple becomes part of her, and whether the category of the human subject might recognise the apple as ‘yet another me, a subject in its own right’ (Mol, 2008: 40). Apples themselves have been shaped by years of cultivation by humans into the forms in which they now exist. In fact they may be viewed as a form of Haraway’s companion species. How then do we draw boundaries around the body/self and the apple? How is the human subject to be defined?

To extend Mol’s analogy, the human subject may be conceptualised as both data-ingesting and data-emitting in an endless cycle of generating data, bringing the data into the self, generating yet more data. Data are absorbed into the body/self and then become new data that flow out of the body/self into the digital data economy. The data-eating/emitting subject, therefore, is not closed off but is open to taking in and letting out digital data. These data become part of the human subject but, as data assemblages also represent the individual in multiple ways that have different meanings based on their contexts and uses. Just as eating an apple has many meanings, depending on the social, cultural, political, historical and geographical contexts in which this act takes place, generating and responding to digital data about oneself are highly contingent acts. If digital data are never ‘raw’ but rather are always ‘cooked’ (that is, always understood and experienced via social and cultural processes), and may indeed be ‘rotted’ or spoilt in some way (Boellstorff, 2013), can we also understand them as ‘eaten’ and ‘digested’?

Haraway and Mol both emphasise the politics of technocultures. Haraway’s cyborg theorising was developed to explain her socialist feminist principles. In all of her work she emphasises the importance of paying attention as critical scholars to the exacerbation of socioeconomic disadvantage and inequalities that may be outcomes of these relationships. Mol similarly notes the political nature of technologies. In her ‘I eat an apple’ essay, for example, she comments about her distaste for Granny Smith apples, once imported from Chile and therefore associated in her mind with repressive political regimes. As she notes, while she may eat this type of apple and while it may nourish her body as other apples do, she is unable to gain sensory pleasure from it.

Data science writings on big data often fail to acknowledge the political dimensions of digital data. They do not see how data are always already ‘cooked’, or how their flavour or digestibility are influenced by their context. Just as ‘eating apples is variously situated’ (Mol, 2008: 29) in history, geography, culture, social relations and politics, resulting in different flavours and pleasures, so too eating data is contextual. Like Haraway’s cyborg figuration (see her interview with Gane, 2006), the digital data assemblage may be viewed both as a product of global enterprise and capitalism and as representing possibilities for radical creative and political possibilities.

Using Mol’s concepts of the eating subject, we might wonder: What happens when we ingest/absorb digital data about ourselves? Do we recognise some data as ‘food’ (appropriate for such ingestion) and others as ‘non-food’ (not appropriate in some way for our use)? Are some data simply indigestible (our bodies/selves do not recognise them as us and cannot incorporate them)? How are the flavours and tastes of digital data experienced, and what differentiates these flavours and tastes?

References

Boellstorff, T. (2013) Making big data, in theory. First Monday, 18 (10). <http://firstmonday.org/ojs/index.php/fm/article/view/4869/3750&gt;, accessed 8 October 2013.

Gane, N. (2006) When we have never been human, what is to be done?: Interview with Donna Haraway. Theory, Culture & Society, 23, 135-58.

Mol, A. (2002) The Body Multiple: Ontology in Medical Practice. Durham, NC: Duke University Press.

Mol, A. (2008) I eat an apple. On theorizing subjectivities. Subjectivity, 22, 28-37.

Mol, A. & Law, J. (2004) Embodied action, enacted bodies: the example of hypoglycaemia. Body & Society, 10, 43-62.

Personal digital data as a companion species

While an intense interest in digital data in popular and research cultures is now evident, we still know little about how humans interacting with, making sense of and using the digital data that they generate. Everyday data practices remain under-researched and under-theorised. In attempting to identify and think through some of the ways in which critical digital data scholars may seek to contribute to understandings of data practices, I am developing an argument that rests largely on the work of two scholars in the field of science and technology studies: Donna Haraway and Annemarie Mol. In this post I begin with Haraway, while my next post will discuss Mol.

Haraway’s work has often attempted ‘to find descriptive language that names emergent ontologies’, and I use her ideas here in the spirit of developing new terms and concepts to describe humans’ encounters with digital data. Haraway emphasises that humans cannot be separated from nonhumans conceptually, as we are constantly interacting with other animals and material objects as we go about our daily lives. Her writings on the cyborg have been influential in theory for conceptualising human and computer technological encounters (Haraway, 1991). In this work, Haraway drew attention to the idea that human ontology must be understood as multiple and dynamic rather than fixed and essential, as blurring boundaries between nature and culture, human and nonhuman, Self and Other. She contends that actors, whether human or nonhuman, are never pre-established; rather they emerge through relational encounters (Bhavnani and Haraway, 1994). The cyborg metaphor encapsulates this idea, not solely in relation to human-technology assemblages but to any interaction of humans with nonhumans.

This perspective already provides a basis for thinking through the emergent ontologies that are the digital data assemblages that are configured by humans’ interactions with the software and hardware that generate digital data about them. Haraway’s musings on human and nonhuman animal interactions (Haraway, 2003, 2008, 2015) also have resonance for how we might understand digital data-human assemblages. Haraway uses the term ‘companion species’ to describe the relationships that the human species has not only with other animal species but also with technologies. Humans are companion species with the nonhumans with which they live alongside and engage, each species learning from and influencing the other, co-evolving. Haraway refers to companion species as ‘post-cyborg entities, acknowledging the development of her thinking since her original cyborg exegesis.

This trope of companion species may be taken up to think about the ways in which humans generate, materialise and engage with digital data. Thrift has described the new ‘hybrid beings’ that are comprised of digital data and human flesh. Adopting Haraway’s companion species trope allows for the extension of this idea by acknowledging the liveliness of digital data and the relational nature of our interactions with these data. Haraway has commented in a lecture that she has learnt

through my own inhabiting of the figure of the cyborg about the non-anthropomorphic agency and the liveliness of artifacts. The kind of sociality that joins humans and machines is a sociality that constitutes both, so if there is some kind of liveliness going on here it is both human and non-human. Who humans are ontologically is constituted out of that relationality.

This observation goes to the heart of how we might begin to theorise the liveliness of digital data in the context of our own aliveness/liveliness, highlighting the relationality and sociality that connect them.

Like companion species and their humans, digital data are lively combinations of nature/culture. Digital data are lively in several ways. They are about life itself (details about human’s and other living species), they are constantly generated and regenerated as well as purposed and repurposed as they enter into the digital knowledge economy, they have potential impacts on humans’ and other species’ lives via the assumptions and inferences that they are used to develop and they have consequences for livelihoods in terms of their commercial and other value and effects.

Rather than think of the contemporary digitised human body/self as posthuman (cf. Haraway’s comments on posthumanism in her interview with Gane, 2006), the companion species perspective develops the idea of ‘co-human’ entities. Just as digital data assemblages are comprised of specific information points about people’s lives, and thus learn from people as algorithmic processes manipulate this personal information, people in turn learn from the digital data assemblages of which they are a part. The book choices that Amazon offers the, the ads that are delivered to them on Facebook or Twitter, the returns that are listed from search engine queries or browsing histories, the information that a fitness trackers provides about their heart rate or calories burnt each day are all customised to their digitised behaviours. Perusing these data can provide people with insights about themselves and may structure their future behaviour.

These aspects of digital data assemblages are perhaps becoming even more pronounced as the Internet of Things develops and humans become just one node in a network of smart objects that configure and exchange digital data with each other. Humans move around in data-saturated environments and they are able to wear personalised data-generating devices on their bodies, including not only their smartphones but objects such as sensor-embedded wristbands, clothing or watches. The devices that we carry with us literally are our companions: in the case of smartphones regularly touched, fiddled with and looked at throughout the day. But in distinction from previous technological prostheses, these mobile and wearable devices are also invested with and send out continuous flows of personal information. They have become the repositories of communication with others, geolocation information, personal images, biometric information and more. They also leak these data outwards as they are transmitted to computing cloud servers. All this is happening in real-time and continuously, raising important questions about the security and privacy of the very intimate information that these devices generate, transmit and archive (Tene and Polonetsky, 2013).

The companion species trope recognises the inevitability of our relationship with our digital data assemblages and the importance of learning to live together and to learn from each other. It suggests both the vitality of these assemblages and also the possibility of developing a productive relationship, recognising our mutual dependency. We may begin to think about our digital data assemblages as members of a companion species that have lives of their own that are beyond our complete control. These proliferating digital data companion species, as they are ceaselessly configured and reconfigured, emerge beyond our bodies/selves and into the wild of digital data economies and circulations. They are purposed and repurposed by second and third parties and even more actors beyond our reckoning as they are assembled and reassembled. Yet even as our digital data companion species engage in their own lives, they are still part of us and we remain part of them. We may interact with them or not; we may be allowed access to them or not; we may be totally unaware of them or we may engage in purposeful collection and use of them. They have implications for our lives in a rapidly growing array of contexts, from the international travel we are allowed to undertake to the insurance premiums, job offers or credit we are offered.

If we adopt Haraway’s companion species trope, we might ask the following: What are our affective responses to our digital data companion species? Do we love or hate them, or simply feel indifferent to them? What are the contexts for these responses? How do we live with our digital data companion species? How do they live with us? How do our lives intersect with them? What do they learn from us, and what do we learn from them? What is the nature of their own lives as they move around the digital data economy? How are we influenced by them? How much can we domesticate or discipline them? How do they domesticate or discipline us? How does each species co-evolve?

References

Bhavnani, K.-K. & Haraway, D. (1994) Shifting the subject: a conversation between Kum-Kum Bhavnani and Donna Haraway, 12 April 1993, Santa Cruz, California. Feminism & Psychology, 4, 19-39.

Gane, N. (2006) When we have never been human, what is to be done?: Interview with Donna Haraway. Theory, Culture & Society, 23, 135-58.

Haraway, D. (1991) Simians, Cyborgs and Women: the Reinvention of NatureLondon: Free Association.

Haraway, D. (2003) The Companion Species Manifesto: Dogs, People, and Significant Otherness. Chicago: Prickly Paradigm.

Haraway, D. (2008) When Species Meet. Minneapolis: The University of Minnesota Press.

Tene, O. & Polonetsky, J. (2013) Big data for all: Privacy and user control in the age of analytics. Northwestern Journal of Technology & Intellectual Property, 11, 239-73.

The thirteen Ps of big data

Big data are often described as being characterised by the ‘3 Vs’: volume (the large scale of the data); variety (the different forms of data sets that can now be gathered by digital devices and software); and velocity (the constant generation of these data). An online search of the ‘Vs’ of big data soon reveals that some commentators have augmented these Vs with the following: value (the opportunities offered by big data to generate insights); veracity/validity (the accuracy/truthfulness of big data); virality (the speed at which big data can circulate online); and viscosity (the resistances and frictions in the flow of big data) (see Uprichard, 2013 for a list of even more ‘Vs’).

These characterisations principally come from the worlds of data science and data analytics. From the perspective of critical data researchers, there are different ways in which big data can be described and conceptualised (see the further reading list below for some key works in this literature). Anthropologists Tom Boellstorff and Bill Maurer (2015a) refer to the ‘3 Rs’: relation, recognition and rot. As they explain, big data are always formed and given meaning via relationships with human and nonhuman actors that extend beyond data themselves; how data are recognised qua data is a sociocultural and political process; and data are susceptible to ‘rot’, or deterioration or unintended transformation as they are purposed and repurposed, sometimes in unintended ways.

Based on my research and reading of the critical data studies literature, I have generated my own list that can be organised around what I am choosing to call the ‘Thirteen Ps’ of big data. As in any such schema, this ‘Thirteen Ps’ list is reductive, acting as a discursive framework to organise and present ideas. But it is one way to draw attention to the sociocultural dimensions of big data that the ‘Vs’ lists have thus far failed to acknowledge, and to challenge the taken-for-granted attributes of the big data phenomenon.

  1. Portentous: The popular discourse on big data tends to represent the phenomenon as having momentous significance for commercial, managerial, governmental and research purposes.
  2. Perverse: Representations of big data are also ambivalent, demonstrating not only breathless excitement about the opportunities they offer but also fear and anxiety about not being able to exert control over their sheer volume and unceasing generation and the ways in which they are deployed (as evidenced in metaphors of big data that refer to ‘deluges’ and ‘tsunamis’ that threaten to overwhelm us).
  3. Personal: Big data incorporate, aggregate and reveal detailed information about people’s personal behaviours, preferences, relationships, bodily functions and emotions.
  4. Productive: The big data phenomenon is generative in many ways, configuring new or different ways of conceptualising, representing and managing selfhood, the body, social groups, environments, government, the economy and so on.
  5. Partial: Big data can only ever tell a certain narrative, and as such they offer a limited perspective. There are many other ways of telling stories using different forms of knowledges. Big data are also partial in the same way as they are relational: only some phenomena are singled out and labelled as ‘data’, while others are ignored. Furthermore, more big data are collected on some groups than others: those people who do not use or have access to the internet, for example, will be underrepresented in big digital data sets.
  6. Practices: The generation and use of big data sets involve a range of data practices on the part of individuals and organisations, including collecting information about oneself using self-tracking devices, contributing content on social media sites, the harvesting of online transactions by the internet empires and the data mining industry and the development of tools and software to produce, analyse, represent and store big data sets.
  7. Predictive: Predictive analytics using big data are used to make inferences about people’s behaviour. These inferences are becoming influential in optimising or limiting people’s opportunities and life chances, including their access to healthcare, insurance, employment and credit.
  8. Political: Big data is a phenomenon that involves power relations, including struggles over ownership of or access to data sets, the meanings and interpretations that should be attributed to big data, the ways in which digital surveillance is conducted and the exacerbation of socioeconomic disadvantage.
  9. Provocative: The big data phenomenon is controversial. It has provoked much recent debate in response to various scandals and controversies related to the digital surveillance of citizens by national security agencies, the use and misuse of personal data, the commercialisation of data and whether or not big data poses a challenge to the expertise of the academic social sciences.
  10. Privacy: There are growing concerns in relation to the privacy and security of big data sets as people are becoming aware of how their personal data are used for surveillance and marketing purposes, often without their consent or knowledge and the vulnerability of digital data to hackers.
  11. Polyvalent: The social, cultural, geographical and temporal contexts in which big data are generated, purposed and repurposed by a multitude of actors and agencies, and the proliferating data profiles on individuals and social groups that big data sets generate give these data many meanings for the different entities involved.
  12. Polymorphous: Big data can take many forms as data sets are generated, combined, manipulated and materialised in different ways, from 2D graphics to 3D-printed objects.
  13. Playful: Generating and materialising big data sets can have a ludic quality: for self-trackers who enjoy collecting and sharing information on themselves or competing with other self-trackers, for example, or for data visualisation experts or data artists who enjoy manipulating big data to produce beautiful graphics.

Critical Data Studies – Further Reading List

Andrejevic, M. (2014) The big data divide, International Journal of Communication, 8,  1673-89.

Boellstorff, T. (2013) Making big data, in theory, First Monday, 18 (10). <http://firstmonday.org/ojs/index.php/fm/article/view/4869/3750&gt;, accessed 8 October 2013.

Boellstorff, T. & Maurer, B. (2015a) Introduction, in T. Boellstorff & B. Maurer (eds.), Data, Now Bigger and Better! (Chicago, IL: Prickly Paradigm Press), 1-6.

Boellstorff, T. & Maurer, B. (eds.) (2015b) Data, Now Bigger and Better! Chicago, IL: Prickly Paradigm Press.

boyd, d. & Crawford, K. (2012) Critical questions for Big Data: provocations for a cultural, technological, and scholarly phenomenon, Information, Communication & Society, 15 (5),  662-79.

Burrows, R. & Savage, M. (2014) After the crisis? Big Data and the methodological challenges of empirical sociology, Big Data & Society, 1 (1).

Cheney-Lippold, J. (2011) A new algorithmic identity: soft biopolitics and the modulation of control, Theory, Culture & Society, 28 (6),  164-81.

Crawford, K. & Schultz, J. (2014) Big data and due process: toward a framework to redress predictive privacy harms, Boston College Law Review, 55 (1),  93-128.

Gitelman, L. & Jackson, V. (2013) Introduction, in L. Gitelman (ed.), Raw Data is an Oxymoron. Cambridge, MA: MIT Press, pp. 1-14.

Helles, R. & Jensen, K.B. (2013) Making data – big data and beyond: Introduction to the special issue, First Monday, 18 (10). <http://firstmonday.org/ojs/index.php/fm/article/view/4860/3748&gt;, accessed 8 October 2013.

Kitchin, R. (2014) The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences. London: Sage.

Kitchin, R. & Lauriault, T. (2014) Towards critical data studies: charting and unpacking data assemblages and their work, Social Science Research Network. <http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2474112&gt;, accessed 27 August 2014.

Lupton, D. (2015) ‘Chapter 5: A Critical Sociology of Big Data’ in Digital Sociology. London: Routledge.

Lyon, D. (2014) Surveillance, Snowden, and Big Data: Capacities, consequences, critique, Big Data & Society, 1 (2). <http://bds.sagepub.com/content/1/2/2053951714541861&gt;, accessed 13 December 2014.

Madden, M. (2014) Public Perceptions of Privacy and Security in the post-Snowden Era, Pew Research Internet Project: Pew Research Center.

McCosker, A. & Wilken, R. (2014) Rethinking ‘big data’ as visual knowledge: the sublime and the diagrammatic in data visualisation, Visual Studies, 29 (2),  155-64.

Robinson, D., Yu, H., and Rieke, A. (2014) Civil Rights, Big Data, and Our Algorithmic Future. No place of publication provided: Robinson + Yu.

Ruppert, E. (2013) Rethinking empirical social sciences, Dialogues in Human Geography, 3 (3),  268-73.

Tene, O. & Polonetsky, J. (2013) A theory of creepy: technology, privacy and shifting social norms, Yale Journal of Law & Technology, 16,  59-134.

Thrift, N. (2014) The ‘sentient’ city and what it may portend, Big Data & Society, 1 (1). <http://bds.sagepub.com/content/1/1/2053951714532241.full.pdf+html&gt;, accessed 1 April 2014.

Tinati, R., Halford, S., Carr, L., and Pope, C. (2014) Big data: methodological challenges and approaches for sociological analysis, Sociology, 48 (4),  663-81.

Uprichard, E. (2013) Big data, little questions?, Discover Society,  (1). <http://www.discoversociety.org/focus-big-data-little-questions/&gt;, accessed 28 October 2013.

van Dijck, J. (2014) Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology, Surveillance & Society, 12 (2),  197-208.

Vis, F. (2013) A critical reflection on Big Data: considering APIs, researchers and tools as data makers, First Monday, 18 (10). <http://firstmonday.org/ojs/index.php/fm/article/view/4878/3755&gt;, accessed 27 October 2013.

Medical diagnosis apps – study findings

Over 100,000 medical and health apps for mobile digital devices have now been listed in the Apple App Store and Google Play. They represent diverse opportunities for lay people to access medical information and track their body functions and medical conditions. As yet, however, few critical social researchers have sought to analyse these apps.

In a study I did with Annemarie Jutel we undertook a sociological analysis of medical diagnosis apps, and two articles have now been published from the study. Annemarie is a sociology of diagnosis expert and we were interested in investigating how these apps represented the process of diagnosis. We drew on the perspective that apps are sociocultural artefacts that draw on and reproduce tacit norms and assumptions. We argue that from a sociological perspective, digital devices such as health and medical apps have significant implications for the ways in which the human body is understood, visualised and treated by medical practitioners and lay people alike, for the doctor-patient relationship and the practice of medicine.

In one article, published in Social Science & Medicine, we focused on self-diagnosis apps directed at lay people. We undertook a search using the terms ‘medical diagnosis’ and ‘symptom checker’ for apps that were available for download to smartphones in mid-April 2014 in the Apple App Store and Google Play. We found 35 self-diagnosis apps that claimed to diagnose across a range of conditions (we didn’t include apps directed at diagnosis of single conditions). Some have been downloaded by tens or hundreds of thousands, and the case of WebMD and iTriage Health, millions of smartphone owners.

Our analysis suggests that these apps inhabit a contested and ambiguous site of meaning and practice. The very existence of self-diagnosis apps speaks to several important dimensions of contemporary patienthood and healthcare in the context of a rapidly developing ecosystem of digital health technologies. They also participate in the quest for patient ‘engagement’ and ‘empowerment’ that is a hallmark of digital health rhetoric (or what I call ‘digital patient engagement’).

Self-diagnosis apps, like other technologies designed to give lay people the opportunity to monitor their bodies and their health states and engage with the discourses of healthism and control that pervade contemporary medicine We found that app developers combined claims to medical expertise in conjunction with appeals to algorithmic authority to promote their apps to potential users. While the developers also used appeals to patient engagement as part of their promotional efforts, these were undermined by routine disclaimers that users should seek medical advice to effect a diagnosis. While the cautions that are offered on the apps that they are for ‘entertainment purposes only’ and not designed to ‘replace a diagnosis from a medical professional’ may be added for legal reasons, they detract from the authority that the app may offer and indeed call into question why anyone should use it.

In our other article, published in the new journal Diagnosis, we directed attention at diagnosis apps that are designed for the use of medical practitioners as well as lay people. We analysed 176 such apps that we found in Google Play and the Apple App Store in December 2013. While 36 of these were directed at lay people, the remainder were for medical practitioners. The Diagnosis article mainly concentrates on the latter, given that our other article was about the self-diagnosis apps for lay people.

Our research suggests that these apps should be used with great caution by both lay people and practitioners. The lack of verifiable information provided about the evidence or expertise used to develop these apps is of major concern. The apps are of very variable quality, ranging from those that appear to have the support and input of distinguished medical experts, specialty groups or medical societies to those that offer little or nothing to support their knowledge claims. While at one end of the spectrum we can see apps as a delivery system for information which has been subject to the conventional forms of academic review, at the other extreme, we see apps developed by entrepreneurs with interests in many topics outside medicine, with little input from medical sources, or with inadequate information to ascertain what the sources might be. The lack of information provided by many app developers also raises questions about how users can determine the presence of conflicts of interest and commercial interests that might determine content.

Managing and materialising data as part of self-tracking

Like many other forms of digital data, self-tracking data have a vitality and social life of their own, circulating across and between a multitude of sites. In a context in which digital data are culturally represented as liquid entities that require management and containment, part of the project of managing the contemporary body is that of containment of the data that one’s body produces. As discursive representations of self-tracking and the quantified self frequently contend, personal data are profligate: it is only right that one should seek first, to collect these data, and second, to manage and discipline the data by aggregating them, representing them visually, and making sense of them.

Shifting forms of selfhood are configured via these digital data assemblages, depending on the context in and purpose for which they are assembled. As the digital data produced by self-tracking are constantly generated and the combinations of data sets that may be brought together on individuals are numerous, personal data assemblages are never stable or contained. They represent a ‘snap-shot’ of a particular moment in time and a particular rationale of data practice. The data assemblages are always mutable, dynamic, responsive to new inputs and interpretations. They thus represent a type of selfhood that is distributed between different and constantly changing data sets. Self-tracking assemblages are constantly created and recreated when information about individuals is derived via digital technologies and then reassembled for various purposes.

Bodies and selves are always multiple, in whatever context they find themselves. However for self-trackers, this multiplicity is foregrounded in ways that may not have occurred in previous eras. If they are reviewing their personal data regularly, they cannot fail to be confronted with the shifting data assemblages that serve to represent their bodies and their selves. Part of the data practices in which they are invited to engage as part of self-tracking culture, therefore, is the negotiation and sense-making around the hybridity and vitality of their data assemblages.

To gain meaning from these data sets, self-trackers or third parties who seek to use their data must engage in sense-making that can interpret these data and gain some purchase on their mutating forms. An important element of self-tracking practices for many people is the visualisation or presentation of their personal data. The notion that data can be beautiful and aesthetically pleasing when presented in appropriate formats pervades data science in general: Gregg (2015) refers to this phenomenon as the ‘data spectacle’. The generation of digital data visualisations can be, variously, acts of work, creative expression and the presentation or performance of selfhood, with the latter an element in particular of self-tracking practices.

In the ‘show-and-tell’ ethos of the Quantified Self movement, finding compelling visual modes to demonstrate the patterns in one’s data is a central feature. The Quantified Self website is full of demonstrations by members of their data, including videos of their ‘show-and-tell’ presentations and still images of their visualisations. Collecting and aggregating personal data, therefore, are part of a range of practices involving self-knowledge and self-expression. By showing one’s data to others in a visually interesting and explanatory graphic, a self-tracker is achieving both self-knowledge and self-expression. Self-tracking becomes performative, both for the insights that a self-tracker may achieve about her or his life but also in terms of the aesthetics of the data that she or he may be able to curate.

The aesthetic elements of data visualisations involve affective responses that may include both pleasure and anxiety (McCosker and Wilken 2014). Indeed McCosker and Wilken (2014) refer to the tendency in data visualisation circles towards the fetishising and sublimity of ‘beautiful data’ as part of exerting mastery over the seemingly unlimited and thus overwhelming amounts of big digital datasets. Extending this logic, the physical materialising of digital data in the form of a 2D or 3D data materialisation may offer a solution to the anxieties of big data. When it is one’s personal data drawn from one’s own flesh that is being manifested in a material digital data object, this may provoke a sense of mastery over what may be experienced as a continually data-emitting subjectivity. The liquidity, flows and force of personal digital data become frozen in time and space, offering an opportunity to make sense of one’s data.

References

Gregg, M. (2015) Inside the data spectacle. Television & New Media, 16 (1), 37-51.

McCosker, A. and Wilken, R. (2014) Rethinking ‘big data’ as visual knowledge: the sublime and the diagrammatic in data visualisation. Visual Studies, 29 (2), 155-164.

Changing representations of self-tracking

I recently completed a chapter for a book on lifelogging that discussed the concepts and uses of data as they are expressed in representations of self-tracking (see here for the full paper, available open access). In part of the chapter I looked at the ways in which people writing about the quantified self and other interpretations of self-tracking represent data and data practices, including in articles published in Wired magazine and other media outlets and blogs.

From the beginning of discussions of the quantified self, the representation of data in quantified self-tracking discourses (as least as it was expressed by its progenitors) included several factors. These include the following: quantified data are powerful entities; it is important not only to collect quantified data on oneself, but to analyse these data for the patterns and insights they reveal; data (and particularly quantified or quantifiable data) are an avenue to self-knowledge; the emergence of new digital and mobile devices for gathering information about oneself have facilitated self-tracking and the generation of quantified personal data; quantifiable data are more neutral, reliable, intellectual and objective than qualitative data, which are intuitive, emotional and subjective; self-tracked data can provide greater insights than the information that a person receives from their senses, revealing previously hidden patterns or correlations; self-tracked data can be motivational phenomena, inspiring action, by entering into a feedback loop; everything can be rendered as data; and data about individuals are emblematic of their true selves.

In more recent times, however, it is evident that a further set of concepts about self-tracked data have emerged since the original euphoria of the early accounts of quantified self-tracking. They include: the meaning of self-tracked data can be difficult to interpret; personal data can be disempowering as well as empowering; the conditions in which data are gathered can influence their validity; the contexts in which data are generated are vital to understanding their meaning; individuals’ personal data are not necessarily secure or private; quantified personal data can be reductive; and personal data can be used to discriminate against individuals.

We as yet know very little about how people are conceptualising and engaging with digital data about themselves. Given the recent scandals about how people’s personal data may be hacked or used or manipulated without their knowledge (the Snowden revelations about security agencies’ use of metadata, the Facebook emotional manipulation experiment, the celebrity nude photo and Sony Pictures hackings, for example), as well as growing coverage of the potentially negative implications of self-tracking as described above, these are pressing issues.

The cultural specificity of digital health technologies

Digital health technologies configure a certain type of practising medicine and public health, a certain type of patient or lay person and a specific perspective on the human body. The techno-utopian approach to using digital health technologies tends to assume that these tacit norms and assumptions are shared and accepted by all the actors involved, and that they are acting on a universal human body. Yet a cursory examination of surveys of digital health technology use demonstrates that social structural factors such as age, gender, education level, occupation and race/ethnicity, as well as people’s state of health and their geographical location play a major role in influencing how such technologies are taken up among lay people or the extent to which they are able to access the technologies.

An American study of the use of some digital health technologies using representative data collected by the National Cancer Institute in 2012, for example, found no evidence of differences by race or ethnicity, but significant differences for gender, age and socioeconomic status (Kontos et al. 2014). Female respondents were more likely to use online technologies for health-related information, as were younger people (under less than 65) and those of higher socioeconomic status. People of low socioeconomic status were less likely to go online to look for a healthcare provider, use email or the internet to connect with a doctor, track their personal health information online, using a website to track to help track diet, weight or physical activity or download health information to a mobile device. However they were more likely to use social media sites to access or share health information. Women were more likely than men to engage in all of these activities.

While there is little academic research on how different social groups use apps, market research reports have generated some insights. One report showed that women install 40 per cent more apps than men and buy 17 per cent more paid apps. Men use health and fitness apps slightly more (10 per cent) than women (Koetsier 2013). A Nielsen market report on the use of wearable devices found that while men and women used fitness activity bands in equal numbers, women were more likely to use diet and calorie counter apps (Nielsen 2014).

As these findings suggest, gender is one important characteristic that structures the use of digital health technologies. The digital technology culture is generally male-dominated: most technology designers, developers and entrepreneurs are male. As a result, a certain blindness to the needs of women can be evident. For example, when the Apple Health app was announced in 2014, destined to be included as part of a suite of apps on the Apple Watch, it did not include a function for the tracking of menstrual cycles (Eveleth 2014). Gender stereotypes are routinely reproduced in devices such as health and medical apps. As I noted in my study of sexuality and reproduction self-tracking apps, the sexuality apps tend to focus on documenting and celebrating male sexual performance, with little acknowledgement of women’s sexuality, while reproduction apps emphasise women’s over men’s fertility.

App designers and those who develop many other digital technologies for medical and health-related purposes often fail to recognise the social and cultural differences that may influence how people interact with them. Just as cultural beliefs about health and illness vary from culture to culture, so too do responses to the cultural artefacts that are digital health technologies. Aboriginal people living in a remote region of Australia, for example, have very different notions of embodiment, health and disease from those that tend to feature in the health literacy apps that have been developed for mainstream white Australian culture (Christie and Verran 2014). It is therefore not surprising that a review of the efficacy of a number of social media and apps developed for health promotion interventions targeted at Aboriginal Australians found no evidence of their effectiveness or benefit to this population (Brusse et al. 2014).

Few other analyses have sought to highlight the cultural differences in which people respond to and use digital health technologies. This kind of research is surely imperative to challenge existing assumptions about ‘the user’ of these technologies and provide greater insights into their benefits and limitations.

My publications for 2014

This the list of my publications that came out in 2014. If you would like a copy of any of the articles, please contact me on deborah.lupton@canberra.edu.au.

Books

Lupton, D. (2015) Digital Sociology (Routledge: this  has a 2015 publication date, but actually was published in November 2014).

Special Journal Issue

Editor of special issue on ‘Beyond techno-utopia: critical approaches to digital health technologies’, Societies (volume 4, number 2), 2014.

Book Chapters

Lupton, D. (2014) The reproductive citizen: motherhood and health education. In Fitzpatrick, K. and Tinning, R. (eds), Health Education: Critical Perspectives. London: Routledge, pp. 48—60.

Lupton, D. (2014) Unborn assemblages: shifting configurations of embryonic and foetal embodiment. In Nash, M. (ed), Reframing Reproduction: Conceiving Gendered Experiences. Houndmills: Palgrave Macmillan.

Peer-reviewed Journal Articles

Lupton, D. (2014) ‘How do you measure up?’ Assumptions about ‘obesity’ and health-related behaviors in ‘obesity’ prevention campaigns. Fat Studies, 3(1), 32—44.

Lupton, D. (2014) The commodification of patient opinion: the digital patient experience economy in the age of big data. Sociology of Health & Illness, 36(6), 856—69.

Lupton, D. (2014) Precious, pure, uncivilised, vulnerable: infant embodiment in the Australian popular media. Children & Society, 28(5), 341—51.

Lupton, D. (2014) Quantified sex: a critical analysis of sexual and reproductive self-tracking apps. Culture, Health & Sexuality, online first, doi: 1080/13691058.2014.920528.

Lupton, D. (2014) Data assemblages, sentient schools and digitised HPE (response to Gard). Sport, Education and Society, online first, doi: 1080/13573322.2014.962496.

Lupton, D. (2014) Health promotion in the digital era: a critical commentary. Health Promotion International, online first, doi: 10.1093/heapro/dau091.

Lupton, D. (2014) Apps as artefacts: towards a critical sociological perspective on health and medical apps. Societies, 4, 606—22.

Lupton, D. (2014) Critical perspectives on digital health technologies. Sociology Compass, 8(12), 1344—59.

Editorials

Lupton, D. (2014) Beyond techno-utopia: critical approaches to digital health technologies. Societies, 4(4), 706—11.

Other Academic Publications

Lupton, D. (2014) Risk. In Cockerham, W., Dingwall, R. and Quah, S. (eds), The Wiley-Blackwell Encyclopedia of Health, Illness, Behavior and Society. New York: Blackwell, pp. 2067—71.

Lupton, D. (2014) Feeling Better Connected’: Academics’ Use of Social Media. Canberra: News & Media Research Centre.

Itinerary for my trip to England in January 2015

Next month I will be visiting England to give talks and meet colleagues. It’s a whirlwind visit, with 8 talks at 7 universities in five days. The itinerary and further details are provided below for those who might be interested in coming along to any of the talks.

Monday 12 January

  • 10.30 am—3.00 pm: NSMNSS Knowledge Exchange Event, London: Speaking on ‘Using social media for academic work – possibilities, benefits and risks’. Further details here.
  • 5.00 pm—6.30 pm: Seminar at UCL, London. Speaking on ‘Fabricated data bodies: reflections on 3D printed digital body objects in medical and health domains‘. Venue: Daryll Forde room, Department of Anthropology, UCL.

Tuesday 13 January

  • 2.00 pm—4.00 pm: Sociological Perspectives on Digital Health event, Warwick University. Speaking on ‘Critical digital health studies: a research agenda’. Further details here.
  • 5.00 pm-7.00 pm: What is Digital Sociology? event, Warwick University. Speaking on ‘What is digital sociology?’. Further details here.

Wednesday 14 January

  • 9.30 am—12.00 pm: Workshop at the Department of Primary Care Health Sciences, Green Templeton College, Oxford University. Workshop topic is ‘Theorising and researching medical and health apps and wearable self-tracking devices‘.
  • 5.00 pm—7.00 pm: Digital Sociology event, Goldsmiths, University of London. Speaking on a panel on ‘Digital sociology, digital cultures, web science, data science  … what’s the difference?’. Further details here.

Thursday 15 January

  • 10.00 am—4.00 pm: ‘Biosensors in Everyday Life’ workshop at Lancaster University. Speaking on ‘Self-tracking cultures: thinking sociologically about the quantified self’. Further details here.

Friday 16 January

  • 12.00 pm—4.00 pm: Yorkshire BSA Medsoc group event, University of York. Speaking on ‘Digital data, big and small: some critical sociological reflections‘. Further details here.