Data literacies for the postdemographic social media self
First Monday

Data literacies for the postdemographic social media self by Anthony McCosker



Abstract
In a postdemographic world, characterized by the continuous production and calculation of social data in the form of likes, comments, shares, keywords, locations or hashtags, social media platforms are designed with techniques of market segmentation in mind. “Datafication” challenges the agency of participatory social media practices and traditional accounts of the presentation of self in the use of social media. In the process, a tension or paradox arises between the personal, curative or performative character of social media practices and the calculative design and commercial usefulness of platforms and apps. In this paper I interrogate this paradox, and explore the potential role of metrics and analytics for emergent data literacies. By drawing together common self-oriented metrics across dominant platforms, the paper emphasizes analytics targets around a) profile, b) activity, c) interactivity and d) visibility, as a step toward developing new data literacies.

Contents

1. Introduction
2. The presentation of the self: Digital agency and datafication
3. Metrics and analytics as the building blocks of data literacy
4. Tactical practices: Profile, activity, interactivity and visibility
5. The indeterminacy of the postdemographic social media self
6. Conclusion

 


 

1. Introduction

The datafication of everyday life through mobile and social media use, or through search and other Internet activity, has become a kind of social fact. Even if the machinations of data science and analytics are not broadly understood, there is a palpable sense of awareness, particularly post Snowden, of the fact of data production, collection, analysis and trade. It may even be having a “social cooling” effect on Internet use (Penney, 2017). Cheney-Lippold pushes this idea to argue that “we are who we are in terms of data.” [1]. Following others like Mayer-Schönberger and Cukier (2013), Cheney-Lippold defines datafication in this context as: “the transformation of part, if not most, of our lives into computable data” [2], which is associated with a data-driven economy, and has become the staging ground for negotiating a life integrated with connective media. The big issue raised by these developments is asymmetry, but also control (see for example Pybus, et al., 2015; Beer, 2016; Andrejevic, 2013). While critiques of platforms’ commercialization are growing, in the era of burgeoning datafication, agency still matters. Gerlitz (2016), for instance, has examined the different socio-technical conditions in which platform data becomes imbued with value. In the move to understand algorithms and big data, we shouldn’t lose sight of indeterminacy, personal practices and “the social” in these emerging socio-technical conditions.

Datafication has also been approached through the more specific notion of postdemographics, which Rogers (2013) has defined as the process of tracing the personal data and profiling practices that have emerged through digital devices, social media platforms and other networked activity. In the context of postdemographics, what do the processes of datafication mean for understanding and researching agency, control and literacies related to the social media self? This paper explores and extends approaches to datafication, metrification and social media analytics with a specific purpose. In addition to synthesizing this critical terrain, the aim is to establish some grounds for repurposing personal analytics as a way of building new forms of data literacies and enhancing participatory agency. Data literacy is positioned here as an emerging sub-field of digital literacies (Calzada Prado and Marzal, 2013; Lankshear and Knobel, 2008), and involves ethical processes of data access as well as personal data rights, interpretation and management with some degree of control. My question is how can we read and make sense of our own data and the data publics that encompass us? How can data literacies help people negotiate their use of social media platforms?

The first step is to embrace the paradox of datafication. José van Dijck articulates this paradox perfectly: social media are both stages for self-expression, communication and self-presentation, and “sites of struggle” between users and platform owners in the control of online identities and digital data [3]. So, the first part of this paper revisits approaches to self-presentation in social media participation, which have never really been naïve to the influence of platform affordances and algorithms, but have begun to be displaced by research focusing on technical controls and surveillance. Agency in digital and social media has been traditionally studied in terms of performance and models of social interaction derived from Goffman to explore the way people negotiate digital contexts and altered private-public boundaries. Self-presentation and curation are core concepts for understanding social media as “participatory”. In fact, agency, choice, reciprocity and performativity are taken as some of its distinguishing features (Lutz, et al., 2014). Others have looked to broader movements such as the open data movement as important forms empowerment, and in the context of datafication, rearticulating participation and devising new forms of “reflexive agency” (Couldry and Powell, 2014; Baack, 2015). More recently, Cheney-Lippold (2017) has described the intentional and unintentional performance of data, where “our algorithmic identities emerge from a constant interplay between our data and algorithms interpreting that data” [4].

The aim of this paper is to explore some key areas for intentional investment in data through the re-purposing of social media analytics, as a form of data literacy. In addition to understanding the paradox of datafication, there is a need to develop tools and tactics to aid data literacy, to foster techniques for reading personal data. By drawing together common social media metrics across a number of platforms, this paper develops a typology of the social media self, targeting a) profile, b) activity, c) interactivity and d) visibility, which act together to produce the core personal and public data formations that matter. This self-analytics focus can supplement critical data investigations to help navigate what I describe as postdemographic data practices or segmentarity, and offer new pathways to data literacy that also involve a crucial element of indeterminacy. For example, in what ways does the cultural encoding and decoding of a “like” equate to a metric of social approval? The aim is to understand the ways hashtags, likes, favourites, location data, filters, algorithms and so on serve not only as the computational tools for managing or curating the flow of social media activity (Hogan, 2010), but also become the manipulable qualities of social media participation and identity. Analytics are commercial techniques but also furnish personal tactics in managing the social media self.

 

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2. The presentation of the self: Digital agency and datafication

In a tradition of research that draws heavily on Goffman’s dramaturgical model of identity construction, social media sites have often been understood as performative spaces that offer their users networked tools for the presentation of self (boyd, 2008; Pearson, 2009; Hogan, 2010; Papacharissi, 2012, 2002; van Dijck, 2013; Markham, 2013). Notions of self-presentation or impression management characterise social media platforms as participatory and user-generated, as spaces for the display, curation and self-arrangement of markers of identity through profile construction, processes of interaction or the strategic exchange of media content. These approaches are still relevant in the context of increasingly sophisticated datafication, despite the emphasis now placed on algorithmic controls, especially if we are to integrate data into digital literacies.

The interactionist ideas of Goffman have often been applied in social media research in order to “conceptualise the play of identity formation on social networking sites”; “Like actors playing a role, [users] can deliberately choose to put forth identity cues or claims of self that can closely resemble or wildly differ from reality” [5]. There is a strong, common sense resonance to this idea that in their material settings and across social media platforms people “construct their identities in reaction to their cohorts” [6]. Goffman’s account of regions and region behaviour carries an explicit, though often under theorised idea of mediation: “Regions vary, of course, in the degree to which they are bounded and according to the media of communication in which the barriers to perception occur” [7]. As one application, Murthy’s (2013) account of Twitter through Goffman’s notion of “embedding” in social communication and self-presentation highlights the (disconnected) situations of speakers and speech, and the fluid “ownership” of utterances.

Platform features, restrictions and affordances shape, but also co-evolve, with user practices, which can be resistant, playful, and reflexive. For instance, on Twitter, playful acts of performativity are used to overcome its restrictions on expression (to 140 characters) and collapsed audiences (Papacharissi, 2012). In mobile and locative media practices, Schwartz and Halegoua identify the presentation of a “spatial self”, which “refers to intentional socio-cultural practices of self-presentation that result in dynamic, curated, sometimes idealized performances of who a user is, based on where they go” [8]. Through the “presentation of location”, (Silva and Frith, 2012), “people share only a portion of their daily life, mostly focusing on physical locations that can shape others’ perceptions of who they are” [9]. Mobility, and mobile imaging constitute significant parts of this intimate and “emplaced” self-presentation (Pink and Hjorth, 2012; Oksman and Turtiainen, 2004). The layering of self-presentational data merges with spaces of urban habitation and activity and with the sensory experiences increasingly captured by mobile media use (Verhoeff, 2012). Similarly, tagging practices, like locative data, codify social media performance even while they give these codified “performative statements of the self greater visibility” (Papacharissi, 2012).

Goffman’s dramaturgical model of self-presentation and mediated communication almost drives analysis to privilege the idea of self-branding (Marwick, 2013) as an active (relentless) process of curating a coherent and successful social media profile. But of course, personal data have also become resources and commodities in the new online economy (Couldry and van Dijck, 2015). In the move from static database to dynamic data stream (Hochman, 2014) computational processes of curation take on a more significant role (van Dijck and Poell, 2013). Algorithms that work to make large social media sites functional and personalized, themselves work as automated curators, as they “selectively bring artifacts out of storage for particular audiences” [10]. Gerlitz and Helmond refer to these shifts as “metrification and intensification”, where a “click on the Like button transforms users’ affective, positive, spontaneous responses to Web content into connections between users and web objects and quanta of numbers on the Like counter”, collapsing qualities such as “excitement, agreement, compassion, understanding”, irony or parody into a single metric [11]. These processes are refined and extended on Facebook through the introduction of additional “reactions”, which work to afford, but also segment emotional responses along very specific, deliberately limited lines (Horning, 2016). Performativity remains, but in an environment algorithmically circumscribed.

Platforms have evolved in a relatively short time-span to offer new modes of understanding social media activity on the basis of associations of interests, actions, locations, and other “postdemographic” factors, and this has implications for how social media use might cohere around categories of personal and social experience. The shift here is from presentations of self to calculations based on increasingly complex and opaque relational factors. The metaphor of social media as front and backstage self-presentation begins to waver, even if the sense of agency and self-curation must remain for social media platforms to continue to be relevant.

 

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3. Metrics and analytics as the building blocks of data literacy

Metrics are, for Gerlitz and Rieder (2014), “epistemic devices that engage in breaking the practices sprawling on social media platforms apart and putting them back together again in various ways”. Measures of social media activity become standardized so that data can be compared and analysed relationally. The aim of this section is to better understand platforms’ slicing and splicing functions not only for the purpose of analysis and user insights, but also as a mechanism for personal agency and intentionality. Data literacy begins with understanding the way digital activities are encoded as forms of knowledge. As a means for knowing digital activities, metrics also feature as ways of being — they become ontological markers — establishing the material logics, limits and defining features of a platform that also work to segment the social media self according to specific patterns of profiling, activity, interactivity and visibility. Table 1 below draws together some of the key metrics around these analytical targets. The aim is to provide a basis for thinking through a new data literacy that can account for these elements of social media practices and the publics that form in relation to them. This involves both the intentional and unintentional performance of self through self-oriented data, and the public contexts that form through the broader spheres of interaction and social media visibility.

These processes both reflect and transform traditional demographics and marketing segmentation. When demographers study and describe populations and the characteristics of societies, they use standard measures: age, employment, education, relationship status, socio-economic indicators, ethnicity, religion, where people live and work, etc. Traditional marketing takes this information and relates it to patterns of consumption in order to better target communication or product design. The shift that has taken place with the granulated data generated through social media and online transactions, sees the rise of “communication optimization” practices that care less about demographics, more about profiles, practices, interests, relatable transactions and the emergence and dissipation of trends. As I will discuss further in the final section, this has been referred to as “postdemographics” (Rogers, 2013). The metrics associated with postdemographics brings great specificity to practices of market segmentation, which have traditionally divided populations along a number of commercially useful lines: geographic, demographic, behavioural, psychographic, occasional, cultural, multi-variable (Tuten and Solomon, 2013). But social media data have privileged more fine-grained access to the behavioural and psychographic forms of knowledge through postdemographic measures. But this goes both ways. Social media users are also interested in connecting through finely segmented “interests”, or in the case of dating apps such as Tinder and Grindr, for instance, personal connections matched on the basis of location, swiping patterns and behaviours including conversations, in addition to profile characteristics (Scott, 2016).

Metrics are the specific measures, the countable elements of social media activity. The difference between metrics and analytics lies in the processing of those designated measures. As Beer puts it, “Metrics facilitate the making and remaking of judgements about us, the judgements we make of ourselves and the consequences of those judgements as they are felt and experienced in our lives” [12]. To counter this metric power through data literacies, we need to take stock of the ways that “in processing data, a platform does not merely ‘measure’ certain expressions or opinions, but also helps to mold them” [13]. To an extent, this is a function of the analytical targets of each metric, each carefully chosen to extract a very specific, useful value — useful, that is, in isolation, in relation to other values, in relation to a singular profile, or in aggregate form across vast numbers of comparative measures. Definitions of social media analytics are often characterized in relation to “an emerging need to continuously collect, monitor, analyse, summarize, and visualize relevant information from social interactions and user generated content in various domains” (Stieglitz, et al., 2014; Stieglitz and Dang-Xuan, 2013). They are insights drawn from the standardized measurement of messy and complex social activity and interactivity, clearly but unapologetically reductive of that messiness and lived experience. A comparison of some of these analytics targets across five different social media platforms is presented in Table 1.

 

Metrics typology and analytics target by platform and selected content and activity
 
Figure 1: Metrics typology and analytics target by platform and selected content and activity.
Note: Larger version of table available here.

 

The typology presented in Table 1 draws together the different platforms’ key use, reach and engagement metrics (sourced from the platform themselves, their developer blogs, and information provided by increasingly powerful third-party analytics providers such as Simply Measured, SocialSprout, Curalate). Each platform has developed features and analytics devices that derive some sort of commercially valuable information. The tools of social media analytics provide the standardized, and usually commercialized basis for making sense of the measures of social media activity. That standardization is evident in the alignment of metric types across the various platforms. And yet, a significant level of indeterminacy remains. What metrics and interpretations matter for developing data literacies around the social media self?

My contention is that data literacies, as a sub-field of digital literacies, can target the operation of metrics and analytics across social media platforms, and can help develop a degree of control over personal social media data practices and visibility. Digital literacies (pluralized to reflect their individual and variable nature) refers to an emerging field oriented toward education and empowerment around issues of online safety, control and productive digital media participation (see for example Lankshear and Knobel, 2008; Jones and Hafner, 2012). Jones and Hafner (2012) see digital literacies as an adaptive set of abilities, skills and knowledge about the operation, use and cultures of digital media. Lankshear and Knobel define digital literacies as plural, practiced and as an expansive politics that requires ongoing advocacy: “Approaching digital literacy from the standpoint of digital literacies can open us up to making potentially illuminating connections between literacy, learning, meaning ... and experiences of agency, efficacy, and pleasure that we might not otherwise make” [14].

Calzada Prado and Mazal (2013) have made significant steps toward defining and outlining the core competencies for incorporating data literacy into digital or information literacy programs, as the domain of libraries, open data movements and eScience. Although they place data literacy within traditions of statistical literacy, they offer a broad enough definition of data literacy as that which “enables individuals to access, interpret, critically assess, manage, handle and ethically use data” [15]. The core competencies involve: understanding data, finding and/or obtaining data, reading, interpreting and evaluating data, managing data, using data (Calzada Prado and Mazal, 2013). This might involve, for example, applying data visualization in ways that can diagram and explore big data and networks as a tool for visual knowledge production (McCosker and Wilken, 2014). There are many roadblocks to data literacies in social media contexts, but most significantly, individuals are often blocked from accessing data by proprietary and commercial restrictions, affecting the potential to understand, read, interpret, evaluate, manage and use that data. The advocacy role of data literacies, alongside the work of the open data movement, is to remove this barrier and improve each of the other competencies. A starting point is to define key analytics targets for the social media self.

Data literacies in the postdemographic era can be built, in part at least, around the analytics targets outlined in Table 1: identity, activity, interactivity and their culmination in visibility, because these are the primary and most clearly measurable categories of self (whether individual or brand). While metrics change according to new platform features (and change their name), the targets of social media analytics remain relatively constant, shaping key indicators of identity and user agency across the dominant platforms.

 

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4. Tactical practices: Profile, activity, interactivity and visibility

This section returns to the central questions: How do we access, read and make sense of our own data and the data publics that encompass us? How do we control the elements of the data self? In the move to the new “culture of connectivity” informed by these fine-grained metrics and analytics targets, data transparency may be the most significant social battle of our time. A key step in fostering data literacies involves controlled, but also better access to social media use-data (particularly our own). Activism and advocacy toward this end has begun to emerge. The open data movement exemplifies this first step of access (see for example Baack, 2015). Pybus, et al.’s (2015) Our Data Ourselves project and MobileMiner app, uses a Comprehensive Knowledge Archive Network platform to harvest data from mobile phones as a starting point for developing individual agency, control and group privacy among their young project participants. With a focus on Facebook’s data collection and targeting practices, and providing a far more detailed account of the metrics listed in Table 1, Share Lab’s “Quantified Lives on Discount: Facebook Algorithmic Factory (3)” (https://labs.rs/en/) illustrates the vast extent of the altered segmentary landscape, and postdemographic environment that social media platforms have brought into being. Here, I explore some of the elements of metrics targeting practices (profiling and activity) and publics (interactivity, engagement and visibility) as a basis for developing tactics for data literacy.

4.1. Practices: Profile building and social media activity

The targets of analytics are those slices of life, translated as social media use, that matter for the purpose of platform efficiencies and development along with network growth, recommendations and market insights. Gerlitz has examined the way platforms “collapse action and its capture into pre-structured data forms which remain open to divergent interpretations”, producing a kind of “grammar of actions” available to both front and back end users [16]. One of the promises of social data is that the metrics and analytics targets, as presented in Table 1, can operate relationally on the basis of a “unified” individual user’s myriad everyday connections and interactions, or autonomously, as aggregated information separated from a specific user profile, furnishing trends and large scale social insights.

Profiling analytics have been central to the value of many social media platforms. That is, specific profile metrics can be identified, but measures of activity and interactivity can also contribute to the layering of a particular profile as well as to the social categorization of objects, topics of interest, events and so on. Developing a social media profile has been a constant and defining feature of dominant social network sites, and has been theorized through the notion of “typing oneself into being” (Sundén, 2003; in boyd and Ellison, 2007). And yet, how this happens establishes significant points of differentiation between, for instance, MySpace, Facebook, LinkedIn, Pinterest, Twitter or Instagram. Interests become a key measurable, and hence functional, feature of profile data in a way that is detached from the more material counters of traditional demographic identity. However, on these measures of profile, dominant platforms like Facebook contrast with those social forums that require or allow anonymity or minimal identity indicators such as 4chan, Reddit, Tumblr and at times Twitter (van der Nagel and Frith, 2015).

Data literacy and agency become necessarily entangled with these processes of data production through digital actions and algorithmic determinants in the different contexts of digital and social media. In part, commercial analytics tools provide a solution to making these elements of datafication at least visible, if not more easily managed. Where social media use data is accessible, a number of third-party tools, like Klout or Tweetstats, have attempted to aid self-evaluation through ranking and visibility metrics (Paper.li, Likejournal, Twylah for Twitter) (Gerlitz and Lury, 2014; see also Hearn, 2010; van Dijck, 2013). Visibility metrics indicate forms of social media presence along with some degree of “notice” (Bruns and Stieglitz, 2013). For native platform analytics, this is exemplified by Twitter’s personal analytics, or Facebooks Insights for Pages, both of which can be used by individuals to measure and understand their social media presence and “performance” (rather than performativity) over time. Gerlitz and Lury emphasise the way these tools also shape and segment users’ actions and interactions in certain ways, orienting social media practices toward increasing scores, network power and ranking, away perhaps from natural communication practices. Nonetheless, through self-analytics tools, for instance with self- and health-tracking devices, “Everyday life — its entities, relationships and processes — are not only captured and represented in abstract graphs, tables and figures, but become negotiable and actionable” in new ways [17].

Forms of activity and content production — pins, posts, tweets, retweets, shares, etc. — drive and populate social media, and are enabled and measured through user practices and analytics. Platforms offer corresponding mechanisms for measuring, comparing and otherwise analysing user activities as a base measure from which to determine the character of a user’s social media presence and performance overtime. Personal analytics will monitor and track the rise and fall of activity with timestamped information, new connections, and details about the precise nature of post type and content. One of the key indicators of activity across platforms for participatory media is that of original content versus re-posted, re-tweeted, re-pinned or otherwise recirculated content. These metrics are oriented around the flow of content through networks, but target and elevate the importance of personal interests in relation to content practices. As a point of focus, Pinterest offers a useful example for putting curatorial activity and interactivity into a non-Facebook context. From 2010, Pinterest re-formulated earlier social bookmarking sites like delicious.com, and was framed early on as a tool for “scrapbooking” and content curation. The company describes it as a “visual discovery tool”, but there has always been a strong sense of self-curation in the activities enabled by the site (Scolere and Humphreys, 2015). A 2012 blog post entitled “Pin-Spiration Nation” captures this common usage quite clearly: “This new virtual scrapbook allows its users to enjoy and share the things that they believe define themselves and that speak to their own individuality” (brookearceneaux.com/). Pinterest’s analytics are designed to connect Web content with users’ own curatorial categories.

The platform only began collecting re-pin and click data in 2014 [18], so much of its focus has been on building its user base and giving an aesthetic, visual form to social bookmarking, making it easy for owners of Web sites to allow pin shares for their content, and for users to be able to curate content into boards according to their own interests and methods. More detailed information is collected on pinners and viewers, to allow demographic insights in terms of country or city location, language, gender, alongside activities such as search data, clicks or purchases. But it’s the pinning activity that counts in terms of the postdemographic measure of interest. As Pinterest’s Guide to Pinterest analytics puts it, “there’s more to a person than where they live or what language they speak. Here you’ll get a deeper sense of the stuff that your audience tends to like” [19]; interests are targeted through tagging categories, curated collections of content in Pinterest’s boards, and their various consumer interests through transactional activity.

4.2. Publics: Negotiating interactivity, engagement and visibility

The analytics targets of engagement, visibility and influence have reshaped notions of participatory interaction along the lines of a less uniform or flat connectivity imperative. Engagement is a measure of social activity (comments, shares and other forms of interactivity), and so has become the measure that counts for professional communicators seeking to activate “audiences”. Engagement usually refers to the unique number of people who interact with content or other users in some way on a site. So what engagement is, how it can be measured, and how it differs across platforms, is of keen interest to those seeking to leverage platforms for personal or commercial gain or influence, or mine them for insights. This is most obvious with Facebook, where likes and reactions constitute the first layer of interaction, and comments and shares are measures of more substantial engagement. Additional layers of processing, for instance in the activities of labelling boards on Pinterest, or tagging content against other users or locations, then offer users room for play and self-curation, often in contrast to those more rigid segmentary codes. These forms of interactivity establish more detailed divisions; for example, on Pinterest, under #health might be: “Lifehack”, “quotes and funnies”, “sadness”, “healthy motivations”, “for work”, “no more jiggle”. This deepens and extends those rigid segments of daily life and states of wellness or conventions for what counts as healthy. In a more general sense, Gehl (2011) describes the value of this interactivity in social network sites as a second order data processing, where users interacting with each other or with content and archives creates value for platforms and users alike.

Engagement, and the forms of interactivity that constitute it, designates the “publicness” of social media’s management of visibility. Visibility is now, arguably, the core value produced through social media sites for individuals, or groups, companies, organizations. Visibility is designated through analytics as a factor of “impressions” or “reach” (terms that are sometimes used interchangeably and vary in their measure for each platform). Impressions and reach refer generally to how many people see posts, and to how many unique users the content spreads. But visibility is generated by the practices of users, the content types, forms of communications, users’ networks and in relation to algorithms designed to manipulate social media feeds often according to predictions about significance or interest, through processes kept secret by platform owners (Bucher, 2012). Twitter, for instance, calculates impressions not through the size of a user’s follower network alone, but in relation to the number of users for whom a tweet appears, including those reached through retweets or hashtag searches, and discounting those users whose account is not in use, for example, because of time zone factors. Engagement signals and defines the intentionality in social media activity as it drives visibility.

Engaging with hashtags or other metatags is a prominent method for achieving, and measuring, visibility and hence publicness (Zappavigna, 2011; Driscoll and Walker, 2014; Bruns and Burgess, 2011; Rambukkana, 2015). Hashtags’ emergence in early uses of Twitter, in fact signals the difficulties of achieving visibility for users wanting to connect with broader publics. Hashtags are also a key generator of personal data on interests and categories, and they are a metric used to define trends or delineate categories of public focus. In Shirky’s (2005) account of early tagging services such as del.icio.us, tagging systems move the Web “towards market logic, where you deal with individual motivation, but group value.” Hashtags “channel” social media activity across networks, or they bundle loosely related content in a manner that makes them technically searchable, attributing greater value to individual pieces of content. For individuals, tagging practices codify social media performance, giving “performative statements of the self greater visibility”, and often entail forms of play, irony or humour (Papacharissi, 2012). We can add to these accounts the role of hashtags as segmentary devices. They connect social media users to others and extend visibility in the form of reach and impressions, but do so by splicing together minimal bits of user activity.

As hashtags encode topics of interest, experiences, events and states of mind, they do so dynamically; these are social codes — both rigid and supple — and in this sense a clear example of the work of “segmentation-in-progress” [20]. So, while metrics capture and categorize practices (connections, profiling and activity), and publics (interactivity, engagement, visibility and influence), they do so in ways leave room for tactical manipulations, or at least retain a crucial indeterminacy.

 

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5. The indeterminacy of the postdemographic social media self

Rogers (2013) coined the term postdemographics to account for what social media market researchers are also starting to notice — platforms organize sociality and activities differently and in ways that continuously generate social and cultural patterns at macro and micro scales. “Postdemographics could be thought of as the study of the data in social networking platforms, and, in particular, how profiling is, or may be, performed” [21]. The concept connects most closely with the traditional marketing notion of “psychographic segmentation” — which slices up markets based on “personality, motives, lifestyles, and attitudes and opinions”, and similar variables and data points [22]. Cheney-Lippold (2017), amongst many others, is critical of the consequences of the delimitation of subjectivity through digital modes of categorization, which denies the lived experience of gender, race, class in favor of personalization processes tied only to data driven “profilization”. However, data literacies can also take into account the indeterminacies involved in these processes.

Ultimately, rather than reveal universal characteristics of populations, through which individuals can be positioned or compared, postdemographics reports on tendencies, trends, interests, tastes, preferences and other items of life moving from share-moment to share-moment. Platforms function and profit as much from the micro elements, transactions and interactions themselves as from unified profiles. Personal experiences are sliced and segmented in the form of tweets, posts, updates, statements, conversations, in photos and videos shared, pinned or re-posted, and in the arrangement of content through feeds. These processes were prefigured in social theory in the early days of the Internet in the work of Deleuze and Guattari (1987). By characterising segmentarity as those social forces that cut across or divide individuals, they sought to understand the operation of power at multiple levels of macro and micro social activity. And their approach accounts for the indeterminacy (they use the term becoming) of these processes as they constitute and re-constitute individuals from one moment to the next.

Deleuze and Guattari nominate three figures of segmentarity — binary, circular, linear — which can take a rigid form that is resistant to change, or a more supple, dynamic and variable form (Deleuze and Guattari, 1987; Bogard, 1998). Binary segmentarity indicates the dominant or rigid categories of social class and identity, of race, gender, age, and so on. But these are also cut across by more supple variations and fluid enactments of those same categories, and alternative markers related to, for example, interests, beliefs, connections, recommendations and the like. Circular segmentarity moves outward around locations, places inhabited, neighbourhoods, cities, states, nations, incorporating different spaces and movements, and so indicates many personal spheres of social media activity delineated by mobile location based data (Baraneche and Wilken, 2015), or the performance of the spatial self. Linear segmentarity refers to the transitions following one state to another and includes all of the procedures and temporal components of social media platforms and activities. This might include events, progressions and transitions or timelines, making use of timestamped digital processes and day-to-day social media utility.

Metric power is difficult to pin down in part because social media activity may designate rigid segmentarity (such as the unitary affirmation of a like or retweet or share), but also suppleness, breaks and recasting (as in some of the creative uses of hashtags, and playful self-presentations, alternative accounts and the use of pseudonymity). User activity, interactivity, visibility and profile management are never completely fixed by platform restrictions or affordances and measurement processes. There is always room for movement between rigid, even binary categories or social codes, conventions, trends and aggregates, and a vast range of more supple modes of self-curation in the slippages, breaks, ironies and differences that come from user practices that work off but also against the segmentary forces of embedded analytics and site affordances. For Baym (2013), skew, non-representativeness, ambiguity or deception feature heavily among social media practices, affecting the credibility and capability of metrics. Outside of consumer activity and reputation management practices, social media users build or curate media in ways that intersect with or break apart so many already over coded, segmented aspects of the self and everyday life.

The point is that one element often missing in analyses of datafication, and postdemographic personalization, or “profilization”, is the indeterminacy and unfinished character of segmentarity. Deleuze and Guattari’s analysis at least points to this “becoming”, or open-ended nature of self-presentation through digital and social media profiling and practices (see also Kember and Zylinska, 2012). Social media performance takes place in relation to continuously segmenting forces and practices, and yet this does not necessarily reduce the utility of social media platforms. It does, however, raise the need for more sophisticated digital and data literacies. This would include a better understanding of the evolving characteristics of postdemographics. My argument is that this can be achieved by re-orienting and even developing new uses for metrics and analytics as a part of digital and data literacies.

 

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6. Conclusion

In a postdemographic world, characterised by the continuous production and calculation of social data as likes, interests, keywords, locations or hashtags, social media platforms are designed with techniques of market segmentation in mind. In this process, a tension or paradox may arise between the personal, curative or performative uses of social media and the design and commercial usefulness of platforms and apps. By drawing together common self-oriented metrics across dominant platforms, I have emphasized analytics targets around a) profile, b) activity, c) interactivity and d) visibility, as a step toward developing new data literacies.

On the one hand, on the basis of the granulated personal data made available developers of third party social media analytics tools have a particular interest in providing insights into user identity and profiling, visibility or “brand awareness”, as well as mapping connectivity and communicative action and interaction. In this sense they are acting as intermediaries between the flow of user data through social media sites, and users who want to leverage that data. Andrejevic, et al. (2015) describe data analytics as “techno-economic constructs whose operations have important implications for the management of populations and the formation of subjects”. Social media analytics are first and foremost techniques of market segmentation, so these tools and the platform features they leverage work to multiply, quantify and in turn deepen processes of segmentation by incremental adjustments to interface design, affordances and subsequent cultures of use (Gerlitz and Lury, 2014; van Dijck, 2013).

But on the other hand, the management and control that results from personal analytics need not be unidirectional. Through social media self-oriented analytics, everyday life is not only captured and represented, but can be negotiated and rendered actionable (Ruckenstein, 2014). In other words, social media are not simply marketing or analytics engines, and hence they persist as near ubiquitous utilities for communication and social exchange. Practices matter, and there is an often-paradoxical reflexive investment in the personal value that can be derived from participating in use-data production, even if this is modified by the associated unintentional data-effects. There is scope here for research that might better understand and present ways of negotiating the factors driving social media segmentation, and enable new digital and data literacies on the basis of this understanding. The analytics targets suggested in this paper that might best inform data literacies are those that target practices (profile, activities), and publics (interactivity, engagement, visibility and influence).

In research, qualitative attention also remains crucial as a way of finding the fault lines, misuses, the conflict and contradictions, moments of resistance or reconfigurations as these become incorporated into the everyday cultures of platform use. If there is a logic to the groupings of analytics presented in Table 1, it lies somewhere between the diversity of processes that constitute self-presentation and self-curation across social media platforms and rigid forms of segmentation. When these indicative metrics are grouped into the four analytics targets delineated above, they highlight the significance of the postdemographic features of the social media self. Grouping metrics by platform around analytical targets also helps us to delineate the segmentary power and trace the operations of different platforms to enable social media data literacies and control. End of article

 

About the author

Anthony McCosker is senior lecturer in media and communication at Swinburne University of Technology in Melbourne, Australia. He is author of the book Intensive media: Aversive affect and visual culture (Palgrave Macmillan, 2013), and co-editor of Negotiating digital citizenship: Control, contest and culture (Rowman & Littlefield, 2016).
E-mail: amccosker [at] swin [dot] edu [dot] au

 

Notes

1. Cheney-Lippold, 2017, p. 11.

2. Ibid.

3. Van Dijck, 2013, p. 199; see also van Dijck and Poell, 2013.

4. Cheney-Lippold, 2017, p. 25.

5. Pearson, 2009, p. 1.

6. Pearson, 2009, p. 6.

7. Goffman, 1959, p. 66.

8. Schwartz and Halegoua, 2015, p. 1,648.

9. Schwartz and Halegoua, 2015, p. 1,649.

10. Hogan, 2010, p. 381; Morris, 2015.

11. Gerlitz and Helmond, 2013, p. 1,358.

12. Beer, 2016, p. 3.

13. Van Dijck and Poell, 2013, p. 10.

14. Lankshear and Knobel, 2008, p. 9.

15. Calzada Prado and Mazal, 2013, p. 126.

16. Gerlitz, 2016, p. 19.

17. Ruckenstein, 2014, p. 80.

18. Pinterest for Business, 2015, p. 4.

19. Pinterest for Business, 2015, p. 17.

20. Deleuze and Guattari, 1987, p. 230.

21. Rogers, 2013, p. 153.

22. Tuten and Solomon, 2013, p. 70.

 

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Editorial history

Received 6 January 2017; revised 18 September 2017; accepted 18 September 2017.


Copyright © 2017, Anthony McCosker.

Data literacies for the postdemographic social media self
by Anthony McCosker.
First Monday, Volume 22, Number 10 - 2 October 2017
http://www.firstmonday.dk/ojs/index.php/fm/article/view/7307/6550
doi: http://dx.doi.org/10.5210/fm.v22i110.7307





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