Social media-fication in the apposphere: Hyperdatafication and the repackaging of personal data

Abstract

In today’s digital landscape, platforms increasingly “feel like” social media. We observe the “social media-fication” of so-called “non-social media” platforms as expressive characteristics of traditional social media platforms are incorporated into mobile apps typically used for other purposes. We argue that this manifests through the repackaging of user behavioral data into expressive social updates, such as GPS maps capturing user movement or the personalized data visualizations of Spotify Wrapped. To examine this phenomenon, we completed platform walkthroughs for three apps: fitness tracking app Strava, payment app Venmo, and music streaming app Spotify. We theorize the concentration and intensification of datafication as a process that we term hyperdatafication. Hyperdatafication emerges through the repurposing of “backend” user data into socially mobilizable data representations, which encourage and further platform engagement within the sociotechnical landscape of affective capitalism. Ultimately, this study raises questions about self-tracking, commodification, and platformed sociality beyond social media.

In the two decades since the advent of mainstream social media, scholars and journalists alike continue to debate what makes a digital platform a social media platform. This discursive boundary work has only escalated in recent years as users seek alternatives to long-standing yet increasingly divisive social media behemoths, sometimes leading them to unexpected digital platforms. Beyond touchstone social media sites like Facebook, Instagram, and TikTok, a broader array of fitness, streaming, education, dating, gaming, and payment apps have become vital to discussions about the true nature of social media. For example, a recent Wired article suggested that the music streaming platform Spotify was “trying to become social media” after it added commenting and polling functionalities for its podcast offerings (Klein, 2024). Similarly, The Guardian asked, “Have hobby apps become the new social media?” when documenting the rise of platforms like the fitness tracker Strava as growing sites for digital community and connection (Stokel-Walker, 2024). The Atlantic has even labeled Venmo as “one of the last real social networks” because its peer-to-peer payment posts capture nostalgic “nuggets of gossip and banal updates” (Kelley, 2024). As new and sometimes unexpected platforms continually incorporate social media-like features, communities, and self-presentation, these articles demonstrate how the boundaries between so-called “social” and “non-social” media have become a key concern of contemporary tech discourse, even as scholarship suggests that these boundaries remain blurred (Sujon, 2021).

Why do so many digital spaces now seem to feel and function like social media, as the aforementioned articles indicate? And what purpose does this shift—evidenced through changing trends in platform design, affordances, and governance—serve within the wider context of affective capitalism and increasing datafication? In this project, we pursue this line of inquiry through the analysis of three applications which may not be straightforwardly classified as social media, but—in line with the above popular narratives—still seem to feel like social media: Strava, Venmo, and Spotify. Each of these free apps is ranked in the top five most popular of its kind by the Apple App Store, but each also focuses on different types of user behaviors on different interactional scales. Strava compiles individual bodily and locational behaviors, Venmo captures peer-to-peer financial exchanges, and Spotify facilitates engagement with the broader music media ecosystem and algorithmic tastemaking. Framing this examination through the lens of affective capitalism (Hearn, 2017; Karppi et al., 2016), we examine social media beyond a specific class of platforms as an ecosystem of characteristic affective experiences enabled within digital environments. By leveraging this “social media-ness,” tech companies stand to strengthen user investment in their platforms while also producing and re-using valuable data.

Ultimately, we find that the “social media-fication” of the app landscape can be attributed in part to the systematic mobilization of users’ behavioral data traces as expressive social updates. The social media experience on “non-social media” platforms is enabled by platform affordances that encourage users to engage with representations of their own behavioral data. These engagements inspire expressive interactions (e.g. re-posting an auto-generated map of one’s morning run) that, in turn, generate even more forms of behavioral data. Examples such as Strava’s fitness activity visualizations, Venmo’s peer-to-peer payment social feed, and Spotify Wrapped’s algorithmic data aggregations showcase “social media-fication” as manifest through what we call “hyperdatafication,” a process of expressive data repackaging layered on top of traditional datafication. We introduce the term hyperdatafication not to suggest a fundamentally novel form of datafication, but rather to demonstrate how regimes of datafication become intensified by re-incorporating the products of datafication as the raw materials of hyperdatafication. By codifying “backend” behavioral data into expressive user-facing updates, platforms stand to further entrench and normalize datafication among their userbase while also extracting further value from surveilling those same users. Thus, we argue that the phenomenon of “social media-fication” can be understood as a contributing element to a wider process of hyperdatafication; said differently, the expressive repackaging of user data which characterizes “social media-fication” lay the groundwork for intensified practices of data collection.

Through this theorization, we expand upon existing scholarship on the datafication of social life, while also contributing to ongoing discussions about the social media-like characteristics that have become embedded in a variety of, sometimes unexpected, platforms. We deconstruct and expand contemporary understandings of social media to draw critical attention to the economic and political implications of everyday digital platforms incorporating expressive social affordances.

The essence of social media

As Sujon (2021) points out, the term “social media” is generally “unquestioned,” referring to “specific digital platforms that enable some form of public-facing social interaction” (p. xxiv). Social media is often defined based on exemplary platforms alone rather than specific characteristics, particularly as the social media landscape is in constant flux with the makeup of platforms frequently changing (Carr and Hayes, 2015). Limited attempts to designate the characteristics of social media have tended to focus on several structural factors. Some accounts emphasize the specific technological features that make a digital space social media, such as the presence of user profiles, social feeds, or liking and commenting opportunities built into the user interface (e.g. boyd and Ellison, 2008; Caraway et al., 2017). Others identify social media platforms based on their technological affordances—the actions that technologies enable and constrain (Bucher and Helmond, 2017; Carr and Hayes, 2015). Characteristic affordances of social media include persistence and replicability that enable social interaction, self-expression, and creativity (boyd, 2011; Sujon, 2021).

However, two aspects are often overlooked in endeavors to identify social media. The first is the affective experiences or feelings that social media engenders, intertwined with platform features and affordances but not confined to any specific digital platform(s). Experiences of community and self-making that arise from interactive features and their connective affordances are paramount to identifying what makes a platform “social media-like” (Butkowski and Corry, 2025; Wilson and Chivers Yochim, 2019). In this view, all platforms have a capacity for “social media-ness,” which is dependent on their design as well as user behaviors and cultures (Papacharissi, 2015). The second aspect is the vital role that data and datafication play in the social media landscape. We build upon Alaimo and Kallinikos’ (2017) claim that social media platforms are first and foremost “data-based organizations” wherein “platform participation is driven toward an endless online conversation . . . through which a computed sociality is made the source of value creation and monetization” (p. 175). They operate within affective capitalism, an economic system predicated on the ability to “capture, structure, and modulate the infrastructures” through which affect moves, such as digital platforms and the Internet (Karppi et al., 2016: 2). Affect is understood here as a sense of collective intensity, energy, and feeling (Papacharissi, 2015). Under affective capitalism, the “social media-fication” of so-called “non-social media” requires capacities for self-expression and social connection with others, a feat which can often be accomplished by adopting data itself as raw material for expression.

While we use the term “non-social media platforms” at times to refer to platforms that are not explicitly designated as social media by tech companies and journalists, we maintain fluidity in the classification of social media given the blurriness of current conceptual boundaries. As is noted by Bhandari and Bimo (2022) social media platforms co-evolve alongside user dynamics, and the convergent nature of platforms and features requires us to analyze social media “not as discrete entities, but rather as moving nodes in a more extensive ecosystem” (p. 10). The process of social media-fication differs from established processes like re-mediation, which describes the re-use of existing forms of media in a new medium, and mediatization, which describes the transformation of societies through media (Couldry, 2008). Instead, in this article, we examine media-fication by tracing how characteristic social media features and affordances are incorporated into apps that are not explicitly labeled as social media platforms, cultivating social media-like expression, identity formation, and social connection. When examining social media as a landscape of qualities that make platform interactions “feel like” social media, so-called non-social media platforms offer generative edge cases that can shed light on social media’s fundamental affective characteristics and continued significance within capitalist systems.

The datafication of life

It is a foregone axiom that the current capitalist system of production has become predicated on the generation, extraction, capture, and commodification of vast amounts of user data (Hearn, 2017; Sadowski, 2019). Many critical scholars have noted the ways that our experiences have been subsumed into sociotechnical systems that thrive on the monetization of our online behaviors (Hearn, 2017). Integral to the process of datafication is the rendering of the continuous variance of our everyday behaviors, feelings, and experiences, into “data,” a “material produced by abstracting the world into categories, measures, and other representational forms [. . .] that constitute the building blocks from which information and knowledge are created” (Kitchin, 2014: 1). Datafication can thus, on one level, be understood as a process of digitization wherein experience is abstracted, discretized, and symbolically represented in a digital form.

However, as Mejias and Couldry (2019) point out, datafication does not only involve a process of digitization. It additionally entails various processes that render human life analyzable. These include making digitizations “indexible and thus searchable” (Mayer-Schönberger and Cukier, 2013: 84); in other words, the integration of symbolic representations into the language of mathematical relationships. Datafication requires the large-scale establishment of infrastructures, systems and operations designed to facilitate the digitization and abstraction of human life, including platforms, apps, databases, and devices that support data capture.

Datafication, sociality, and expressivity

As an assemblage of material and affective elements that work together to make human behaviors available for capture and analysis, datafication is a social process configured around the new relationships and conversations that emerge as data becomes a central element organizing human interaction. For instance, Cheney-Lippold (2017) outlines the role of algorithmic categorization in shaping identities and behaviors, arguing that these systems produce a new form of “algorithmic identities.” Rather than capturing who individuals are, these identities are constructed based on patterns in data—what users do, where they go, what they buy, and who they interact with online. These algorithmic identities are then used to predict (and thus influence) behaviors and preferences, serving as the basis for personalized marketing, content curation, and even credit scoring or employment decisions (Cheney-Lippold, 2011; Cohn, 2019). As such, datafication produces sociality, in addition to “capturing” it. Datafication is in this sense social in its own right; it structures the ways that humans interact and form identity and community. Data itself is expressive insofar as it involves the process of symbolic representation and the integration of these representations into a relational system.

Datafication translates continuous, complex human phenomena into a form of language that is understandable and usable by machines—a language that remains expressive only in the backend, for the computer itself (Knorr and Pentzold, 2025). This language is composed of an assemblage of code that embodies sets of associations and correlations and transforms human behavior into a form that is analyzable and actionable (Cheney-Lippold, 2017). Data, then, is a construction. It naturalizes a particular perspective of the world, presenting itself as neutral or objective. However, in reality, data is always a curated representation—one that selectively includes and excludes certain aspects of human life (Kitchin, 2014). As such it is always-already expressive and social, shaping and conveying particular meanings while obscuring others.

The expressiveness of data gains an additional valence through user engagements with the products of datafication, as seen in practices such as self-tracking and self-quantification. Self-tracking involves “regularly monitoring and recording, and often measuring, elements of an individual’s behaviors or bodily functions” (Lupton, 2016: 12). The rise self-tracking technologies sparked the Quantified Self Movement—a community focused on biometric self-tracking with the tagline, “self knowledge through numbers” (Barta and Neff, 2015), which further highlights the growing involvement of individual users and self-tracking practices in the datafication cycle. In addition, Grootens (2024) examines the practice of expressive self-tracking, or utilizing self-tracking practices and technologies, such as GPS data, to express personal ideas and creative sensibilities.

In this article, we draw on these literatures to examine the ways that “non-social media” platforms systematically repackage user behavioral data to construct social media-like interactions and expressions that ultimately serve affective capitalism. The present study examines two key research questions: (1) How do so-called “non-social media platforms” leverage the feelings, aesthetics, and experiences of social media through the incorporation of specific features, affordances, and affect? (2) What are the implications of this transformation within a broader system of affective capitalism?

Research design

We approach the above research questions using case studies of three popular “non-social media” applications. While these applications are dedicated to facilitating user behaviors that are not generally shared as public social performances online, they nevertheless, possess interactional characteristics that encourage user investment and ritual engagement (Caraway et al., 2017). Although none are designated as “social media” in the Apple App Store, they incorporate sociality and expressivity into the everyday usage of platforms that center on typically private, personal, invisible, or offline user behaviors such as exchanging money or listening to music (Swartz, 2020).

Our three cases of interest are free apps designated among the top five most popular in their genre, including: (a) Strava, a “health and fitness” activity tracker that shares self-quantification metrics and GPS movement data with friends; (b) Venmo, a “finance” app that displays transactions between friends as social updates; and (c) Spotify, a “music” streaming service that allows users to observe what their friends are listening to. We selected these cases to balance similarities in the expressive qualities and popularity of each app with their differences. The apps capture different kinds of personal data at distinct levels of interaction, including individual bodily and spatial information, peer-to-peer monetary exchanges, and engagements with the broader algorithmic entertainment ecosystem.

To understand how our app case studies navigate and exploit the expressive component of behavioral data, we undertook platform walkthroughs of each app (Light et al., 2018). Drawing from critical data studies and science and technology studies, platform walkthroughs provide a systematic method for immersive engagement with mobile applications based on components of their “environment of expected use” comprising app vision, operating model and governance alongside a “technical walkthrough” that traces usage processes of registration and entry, everyday use, and discontinuation of use (Light et al., 2018: 881). The walkthrough method enables critical analysis of “the relatively closed systems” of apps “to understand how [they guide] users and shape their experiences” (Light et al., 2018: 881–882).

To support sustained engagement with each platform (Charmaz, 2014), we completed the walkthroughs over a 6-month period with an initial exploratory analysis between September 2021 and February 2022 and then a secondary analysis between September 2024 and February 2025. The authors did walkthroughs of all three platforms, with each author taking one platform as an area of focus. After the walkthroughs were complete, we conducted detailed textual analysis of notes and screenshots gathered during the walkthroughs (Kuckartz, 2014). We discussed emergent thematic categories in detail to come to an agreement on our findings (Charmaz, 2014).

Findings

In the following section we present the results of the respective case studies focusing on how each of the three studied platforms uniquely repackage behavioral data for expressive purposes. Through this analysis, we argue that “non-social media” platforms display mechanisms and discourses of “expressivity,” and posit that this mobilization of data for expressive purposes ultimately serves to normalize datafication as an element of affective capitalism. Specifically, in the context of each of the above platforms we theorize that (a) digital platforms are engaging in the capture and quantification of “offline” activities, converting these activities into data (i.e. traditional datafication); (b) this data, which previously existed as engagements with the platform, becomes repurposed into explicitly social and expressive data; and (c) becomes re-introduced into the social ecology of the app as mechanisms of social engagement with other users, often as high status data displays. In describing our findings, we will outline how this theorization, which expands on the foundation of datafication, manifests differently on each of the three platforms analyzed and highlights its implications for broader literatures.

Strava: self-tracking as sociality

Strava is an online social fitness network released in 2009. The platform positions itself as a community-driven, data-focused fitness app that emphasizes not just personal performance but also the social aspects of physical activity. We observed in our walkthroughs that Strava’s branding highlights its utility for both casual users and competitive athletes, offering features that appeal to a wide range of fitness levels. The use of vibrant, motivational imagery and messaging in its onboarding and marketing materials reinforces this identity, presenting Strava as a tool for achievement, growth, and connection (see Figure 1).

Figure 1.

Strava onboarding and challenge features.

Through strategic partnerships and influencer marketing, Strava has cultivated a strong presence in fitness culture, promoting its features through both professional athletes and everyday fitness enthusiasts (see Figure 2). We found that Strava enables users to track a variety of activities—such as cycling, running, and swimming—via GPS route mapping and metrics like distance, time, and heart rate. These activities are recorded and rendered into sleek visual representations and then shared through a central social feed where users can interact with others’ posts. We noted that users can join Strava with their pre-existing Google or Facebook accounts, which further embeds the app within broader digital ecosystems and social infrastructures. The walkthrough revealed several design choices that reflect common social media aesthetics. Notably, Strava’s interface shares a number of similarities with that of TikTok; for instance, through the positioning of the “record” button, which is featured prominently in the center-bottom of the screen, as if inviting users to begin their self-tracking. Once we completed a workout, the app generated visualizations such as GPS maps and weekly snapshots. These can be easily shared to the user’s feed as stylized social content, designed to be liked, commented on, or given “kudos.” Although the app allows users to set their workouts to “private,” we observed that Strava’s culture is one of socialized physical activity, wherein social connection through shared workouts or location data is heavily encouraged as one of the app’s main utilities, as demonstrated by the marketing materials displayed in Figure 1.

Figure 2.

Sample marketing campaigns.

Strava is one of many social fitness applications which partake in the “quantified self” movement; it is part of a wider ecosystem, which aims to digitally capture experiences relating to physical health and fitness (Couture, 2021). Scholarship has noted how many “traditional” fitness apps capture experience through datafication, making resultant data legible within the backend of the app. While traditional fitness apps focus more narrowly on personal data and health metrics with limited options for external sharing, our analysis found that Strava differs from such traditional apps by actively foregrounding itself as both a fitness tracker and a social platform. The home page of the app is a dynamic social feed, akin to Facebook or Instagram. This positions the user’s data not as static information but as content: workouts become posts; achievements become badges of honor; maps become aesthetic expressions of bodily discipline and spatial movement. This shift triangulates biometric and location-based data into social updates and networking vectors. For example, after one of the authors completed a run in Strava, the app automatically produced a social page describing the activity, which included space for a title, description, photos, an option to tag other users, and an interactive map. With user permission, all of this information can be shared on public group or “club”-based feeds and profiles (see Figure 3). With Strava, user motion and behavioral data recorded in the app is the social currency that powers its expressive and connective features.

Figure 3.

Sample Strava share feed.

We found that this repackaging of data as social updates, such as “activity posts” and “achievements,” adds value to the platform by creating a sense of community and competition. Users are motivated to return to Strava through features like leaderboards, a digital trophy case, segment challenges, and personal records (PRs), which facilitate social comparison and self-improvement. The resulting sense of accomplishment, reinforced by social validation from friends and followers, encouraged repeated use. Couture (2021) points out that, while the application of a social network model to fitness tracking can indeed be a source of motivation, it also introduces technologically mediated surveillance strategies that “encourage and reward displays of bodily self-discipline” (Couture, 2021: 184).

Strava’s everyday usage hinges on its ability to repurpose personal locational and bodily data into social content that fosters continuous user engagement. By transforming GPS coordinates and performance metrics—such as pace and distance—into visual representations like maps and elevation profiles, Strava’s affordances position it as a site for acts of expressive self-tracking. GPS maps of one’s exercise are not simply records of one’s behavior; they become artifacts of self-representation. Moreover, the integration of these social updates with AI-powered features—such as personalized workout recommendations or analysis of performance trends—enhances the user experience, encouraging further interactive forms of socially shareable units of self-tracking. This tension was present throughout our walkthroughs: Strava offers users tools for connection and motivation but does so by harnessing the same emotional and visual economies that define more traditional social media platforms.

Venmo: forcing networked expression through everyday transactions

Venmo is a US-based peer-to-peer payment app founded in 2009 with about 68 million active monthly users as of 2025. It allows people to easily transfer money “to pay friends, local businesses, charities, and more” (Venmo, n.d.). Venmo is perhaps the most successful example of peer-to-peer payment apps, a genre of app which is invested in the datafication and digital capture of financial transactions (Swartz, 2020). By incorporating features from social media platforms like Facebook and X, including user profiles, friends lists, and a social feed (Acker and Murthy, 2020), the app makes “visible the often invisible social components of money” (Kelley, 2024). According to Swartz (2020), “Venmo is not a wallet; it is a conversation” (p. 132). Instead of the day-to-day opinions, stories, memes, and photos that populate the prototypical social media feed, Venmo’s home feed is populated with other people’s financial transactions. All social media platforms capture, share, and sell users’ engagement with platform features and, by extension, other users (Hearn, 2017). For traditional social media platforms, this user engagement is primarily expressive in nature, but for Venmo, user engagement is primarily transactional with expressive qualities attached.

Through its affordances, Venmo requires users to actively participate in the repackaging of peer-to-peer monetary transactions into social status updates and commodifiable traces of interaction. This manifests through Venmo’s transaction description feature, which prompts users to enter an explanation for each payment that they send or request on the app. Descriptions allow for clear labeling of payments as a tool for personal finance, but they also offer a social capacity that has inspired dozens of articles about “Venmo stalking” and “Venmo FOMO” as well as investigations to uncover the public Venmo accounts of celebrities and politicians (Malone Kircher, 2019; Ohikuare, 2018). The home feed includes only the names of users who exchanged money and their written descriptions without specifying the dollar amounts exchanged. It also enables username tagging, liking, and commenting on others’ transactions. While these transactions can now be set to private, they are public by default. On Venmo, witnessing friends’ social transaction narratives—divorced from their monetary value—is a precondition of usage.

Venmo’s branding indexes its networked social uses and their performative, expressive implications. During our initial analysis in 2022, Venmo’s marketing images included a slide deck that carries over stylistically into its user login page. As shown in Figure 4, the images model the Venmo social feed and transaction description practices. The app store slides include payments from users with straightforward explanations such as, “June rent ” or “Aquarium” and more abstract, playful descriptions, such as “Dumplings all day ” or “Best concert. Best friend. Even though it rained. And you forgot to pack umbrellas. Love you. .” These captions are not merely one-to-one descriptions of monetary exchanges. They are also complex multimodal digital narratives, which may serve to embellish and sometimes obfuscate the original purpose of the transactions in question.

Figure 4.

Venmo app store illustrations and sign in screen modeling the app’s social feed and potential uses (e.g. social transactions, cryptocurrency, Venmo credit card).

For the purposes of observing the Venmo feed, we used Facebook Connect and shared contacts features to establish an automatic friends list. Our feed (see Figure 5) quickly became populated with posts similar to those in Venmo’s marketing content. Users leveraged the networked, social nature of transaction descriptions to encourage digital intimacies through ironic humor and lightly coded messages about the purposes of their monetary exchanges. When transmitting funds on Venmo, the app encourages users to describe their transactions with emojis rather than text. When users type in specific words, such as “rent,” “bills,” “pizza,” or “party,” the app suggests emojis, which, if selected, will replace the typed text. Many posts on the Venmo feed are captioned solely with emojis or with ironic and vague captions, such as “dancin on chairs.” In one author’s everyday use of Venmo to pay a friend for dinner, we negotiated between the desire to create an interesting, connective description of the transaction that recorded our time together and the desire to preserve some level of privacy from our shared Venmo “friends.” We settled on the cryptic caption, “.” Another friend who made a game of prioritizing misleading, relationship-building transaction descriptions wrote, “What is it for? What is anything for? Why are we even here?” to exchange a small sum for tickets to an event.

Figure 5.

Venmo home feed and user interface displaying friends’ public transactions (left) and privacy settings options when creating a Venmo account (center) with Venmo payment screen and emoji suggestions (right).

Ultimately, Venmo leverages the social nature of monetary exchange by requiring users to repackage their transactions into shareable status updates in a social media format. The app centers on attaching networked, expressive qualities to peer-to-peer payment, an interactional format that is always social and relational but rarely understood as performative or expressive. The Venmo feed fosters engagement with the app and encourages users to key their behavioral data as an identity performance for networked observation. By requiring payments to become expressive social updates, Venmo mandates this form of datafied expression as a social currency.

Spotify: all data can be repackaged

Spotify is an audio streaming platform founded in 2006. As of June 2024, it is one of the largest providers of music streaming services, with over 626 million monthly active users and 246 million paying subscribers. Initially positioned as a platform for finding and streaming music, Spotify’s functional niche in the music space has significantly expanded over the past decade. Features such as personalized recommendations, algorithmically generated playlists like “Discover Weekly,” and curated genre-based stations have become central to its identity. The platform is now positioned not merely as a music repository but as a mediator of individualized music consumption. Spotify has garnered attention in scholarly literature for its role in shaping trends within the music industry, with scholars noting its influence on algorithmic cycles and user taste (Hesmondhalgh and Meier, 2018; Prey, 2018). Through its recommendation systems, Spotify has been recognized for actively shaping listening habits, creating what Lobato (2019) describes as “algorithmic trend cycles,” wherein user preferences are continuously updated based on past behavior. In parallel, researchers have explored the social dimensions of streaming services, highlighting features such as social sharing, collaborative playlists, and real-time friend activity feeds that blend music discovery with social interaction (Baym, 2018; Bonini and Gandini, 2019). These affordances facilitate the transformation of listening from a personal experience into a mediated and networked social practice, where users perform identity and taste publicly. The sharing of listening data, as seen in phenomena like Spotify Wrapped, has been theorized as a form of algorithmic self-expression and social currency, linking personal taste with social recognition and cultural capital (Eriksson et al., 2019; Prey, 2020).

Through our walkthrough of the app, we observed how Spotify embeds ambient social affordances within the core listening experience. Users are able to follow friends, linked through Facebook or email, and receive a real-time feed of their listening activity through the Friend Activity tab. This feed appears persistently on the right side of the desktop interface and shows what friends are currently listening to, often including time stamps and track information. We noted that the Friend Activity feed blends music discovery with ambient social engagement. While not designed for active conversation, the feed allows for a form of passive presence—users “see” each other through music, creating a kind of backgrounded sociality that operates in tandem with the individual act of listening. During everyday use this allowed the authors to learn intimate details about Spotify friends’ musical tastes, listening habits, including tendencies to repeatedly listen to the same songs. Spotify also includes a “Private Mode” function, allowing users to temporarily hide their activity. This opt-in/opt-out mechanism reveals a subtle layer of negotiation: users can selectively choose when their listening becomes visible to others, transforming a personal act into a potentially expressive or performative one.

Spotify’s branding and marketing materials further frame the platform as a socially meaningful space. The platform repeatedly emphasizes music as tied to emotion, identity, and connection. One of the most vivid examples of this branding strategy is Spotify Wrapped. Introduced in 2013, Wrapped exemplifies Spotify’s capacity to turn user data into a social product. Each December, users are presented with an interactive slideshow that visualizes their listening patterns from the past year. Through our use of the wrapped feature, we found that Wrapped highlights top artists, favorite genres, number of minutes listened, and more. These metrics are designed for visual impact, using Spotify’s signature bold color palette, dynamic animations, and stylized typography (See Figures 6 and 7).

Figure 6.

Spotify’s graphic explanation of the Friend Activity Tab.

Figure 7.

Sample of a single Wrapped infographic (left) and of Wrapped within the Spotify app (right).

We also observed how Spotify Wrapped incorporates a comparative, almost competitive dimension to this data. Users are sorted into aesthetic groups based on listening habits, and receive percentile rankings—such as “you’re in the top 0.5% of Taylor Swift listeners”—that position personal data within broader social hierarchies. These rankings offer playful ways to distinguish oneself both from friends and from imagined others while still fostering identification through shared taste. Importantly, Wrapped is optimized for sharing: users are encouraged to post slides directly to other platforms like Instagram, TikTok, or X. This circulation of data-as-content turns Spotify into a cross-platform social actor, using personal listening behavior to generate brand visibility and foster viral engagement. In effect, Spotify Wrapped transforms listening into a kind of seasonal ritual and music taste into a public-facing form of cultural capital (Eriksson et al., 2019).

This walkthrough underscores how Spotify takes up the visual language and affective dynamics of social media. Each slide of Wrapped mirrors the image-forward, bite-sized logic of Instagram Stories or TikTok clips—designed to be consumed quickly, shared widely, and emotionally resonant. Beyond Wrapped, Spotify’s mobile interface features large icons, personalized widgets (e.g. “Made For You”), and push notifications that prompt users to revisit playlists or discover new tracks. These elements further contribute to the app’s ongoing transformation of music consumption into a socially situated and algorithmically mediated experience.

Wrapped, in particular, reveals how data becomes not just information but expression. The feature invites users to perform identity through music—aligning themselves with artists, scenes, and subcultures, while also curating a narrative of taste. A profile in The New Yorker captures this dynamic, noting that “These statistics were intended to communicate a person’s unique taste and curatorial acumen, providing a coherent narrative to the often-random experience of consuming music online” (Brickner and Wood, 2024). From our analysis, Wrapped becomes a prime example of what Annabell and Rasmussen (2025) term an “algorithmic ritual”—a routine through which users reflect on and display their platform-mediated selves. Through Spotify, we see how everyday engagement with personal data through listening time, favorite genres, replayed tracks, is continually reframed as a social and cultural event. Spotify exemplifies how non-social media platforms leverage the feelings, aesthetics, and experiences of social media to transform private habits into publicly visible, shareable moments.

Discussion

In this article, we sought to explore the popularly observed phenomenon we termed “social media-fication” through an excavation of the characteristics that make a so-called “non-social media platform,” or a platform that is not typically branded as social media, “social media-like.” It is perhaps worth clarifying differences between “social media-fication” and related concepts such as mediafication and re-mediation (Couldry, 2008). Dynamics of mediafication and remediation are relevant along multiple stages of the datafication process. For example, the transformation of captured data from user behaviors into media artifacts (e.g. Strava’s GPS maps) constitute a process which is intimately involved in the dynamic of “social media-fication.” In addition, remediation, the re-use of existing forms of media in a new medium, is similarly relevant to our description of the re-purposing of captured data as new expressive interactions.

However, we deploy “social media-fication” to describe a broader phenomenon wherein tech companies leverage the infrastructural and experiential essences of social media to repurpose “non-social media” apps with social and identity-oriented characteristics that could enhance user investment. We find that the collection, repackaging, and resocialization of user behavioral data (e.g. peer-to-peer payment transactions, fitness activities, and music streaming) is at the heart of the “social media-fication” transformation. We argue that the observed dynamics in the social media-fication of “non-social media” platforms amount to a process we term “hyperdatafication,” which renders the products of datafication newly expressive. In other words, the transformations in platform design and affordance structure which are associated with “social media-fication” (namely, the collection, repackaging, and resocialization of user behavioral data) contribute to a wider process of “hyperdatafication,” which can be understood as an intensification of previously observed dynamics of “datafication.”

From datafication to hyperdatafication

As discussed, datafication goes beyond the mere digitization of life. According to Mejias & Couldry (2019), datafication involves both “the transformation of human life into data through processes of quantification, and the generation of different kinds of value from data” (p. 3). In particular contexts, the operations of datafication are deployed to create artifacts or representations that are accessible and understandable to humans. These representations can be seen as the outcome of a double process of translation. First, human behavior is translated into machine language, allowing it to be analyzed. Then, after analysis, it is translated back into human language in the form of data representations which may in turn give rise to new opportunities for social interaction. One might see this reflected in phenomena such as “expressive self-tracking,” wherein users mobilize datafications in their own expressive engagement on the platform.

It is precisely at this intersection of data processing and human engagement that our research is situated. We propose hyperdatafication as a term to describe a nuance within datafication, for the purposes of both heightening analytical clarity in scholarly literature and highlighting nuances within the wider regime of big data. We suggest that hyperdatafication refers to the ways that platforms systematically mobilize “backend” user data as a newly social resource for user engagement, rendering datafications of users’ everyday digital experiences and behaviors newly expressive.

While users have long interacted with data representations for the purposes of identity formation and self-expression (e.g. the quantified self and expressive self-tracking), we emphasize that what is novel in this context is the capture and facilitation of such behaviors by platforms themselves—in other words, the integration of such interactions into the structure of the platform, rather than occurring “paratextually,” driven primarily by users. This platform-led re-purposing of data representations can be seen in “social media-like” features such as Spotify Wrapped (which encourages users to engage with their own data in a socially expressive manner), the Strava “home feed” (wherein data representations are deployed as social updates, and this expressive re-purposing of previously captured data makes up one of the intended purposes of the app), and Venmo’s “social transaction updates” (wherein previously captured transactional data is repurposed as social updates which are framed as a central feature of the platform). While there exists a certain degree to which users can “opt-out” of such features (e.g. by setting payments to private), such efforts would be in contrast to the “normal usage” models encouraged by platforms.

These features can be understood as “datafications of datafications” according to the following conceptual framework:

1. Human experience is captured through datafication (the quantification of human activity and the infrastructure that supports this capture).

2. Human experiences of datafication (seen in phenomena such as the quantified self and expressive self-tracking).

3. The datafication of the experience of datafication (the implementation of systems that facilitate the capture of data on data-driven interactions, such as social feeds built around data representations like GPS maps or transaction records, all aimed at further quantifying social experiences).

Building on the definition of datafication as a process involving the creation of infrastructure for data capture, we argue that this intensified stage of datafication marks an effort by platforms to implement infrastructure that captures the new forms of sociality, expressivity, and experience that emerge from the reintegration of previously captured data into social contexts.

We propose “hyperdatafication” not to suggest a chronological development or evolution of datafication, but rather to bring into relief the processes through which regimes of datafication propagate themselves and transform that which they extract. It can thus be understood as an intensification of datafication—a process which can be broadly identified through the iterative enclosure and re-incorporation of the very products of datafication as the raw materials of hyperdatafication. We propose “hyperdatafication,” as a way to capture these multiple layers of datafication at play and their role in codifying digital data extraction as a tenet of social life. Thus, just as Goffman (1979) discussed hyper-ritualization as a ritual that “becomes itself ritualized” and Baudrillard (1981) described hyperreality as “more real than real,=” we observe data itself becoming datafied. Hyperdatafication exists as a simulation of data and datafication with the capacity to transform society by altering structures of meaning and feeling under capitalism. Goffman argued that hyper-ritualization is “the shadow and the substance” of existing societal power structures because it contributed to normalizing them as cultural defaults (p. 6). Hyperdatafication is also the “shadow and the substance” of datafication by both perpetuating the continuous extraction of user behavioral data and promoting datafication as a normal and sometimes even playful part of everyday life.

Our discussion of hyperdatafication contributes to research problematizing the unambiguous distinction between “life” and the processes by which it is datafied—or, said differently, the division between world and representation. For example, Kitchin and Dodge (2014) notably mobilize the concept of transduction, borrowed from Simondon, to argue that code continually “brings into existence” space through “everyday transductive practices”; the form, function, and meaning of space are transduced by code. Hyperdatafication interfaces with these wider discussions by articulating a particular context and mechanism through which computation, rather than just representing human behavior, is involved in generating human behavior itself (in this case, through the generation of new kinds of opportunities for sociality and expressivity). Thus, we argue that one of the consequences of datafication is the emergence of a “meta” domain of experience—one that generates increased opportunities and channels for sociality by integrating the products of social data capture.

Hyperdatafication and value

One of the core components of datafication is the transformation of human behavior into data for the purpose of generating value (Knorr and Pentzold, 2025). While there is debate over how exactly data generates value (Zeng and Glaister, 2017), past scholarship has argued that data collection can lead to value generation through three processes: (1) The improvement of platform operations in the backend (which creates business value by increasing user retention and satisfaction); (2) personalized recommendations, which lead to increased consumption and sales; and (3) targeted advertising, which may increase advertising revenues (Fast et al., 2023).

Thus, the drive toward hyperdatafication facilitates an increase in the kinds and amount of behavioral data available for such forms of value generation. While non-social media platforms offer behavioral data in the form of “tracking users’ browsing behaviour,” social media platforms engender behaviors wherein data is “actively provided by users themselves” (Fast et al., 2023: 202). Thus, the social media-fication of these platforms, aided by the mobilization of data representations as opportunities for expression, expands the grounds for social capture via the introduction of new kinds of affordances for social experience. Hyperdatafication thus offers a fourth “pathway” for value generation: the re-incorporation of data representations into the environment of the platform. This generates value by increasing the scope, quality, and quantity of the kinds of data available for value extraction.

Hyperdatafication can be situated as emerging from a paradigm wherein data is understood as capital rather than commodity. Sadowski (2019) points out that, “until recently, companies simply deleted data or chose not to collect it because paying for storage did not seem like a good investment” (p. 1). This is no longer the case; now, “companies are clamouring to collect data—as much as they can, wherever they can” (Sadowski, 2019: 1). This difference reflects a subtle shift in the logic and operations of the “data economy.” Whereas data was once a means through which to accrue capital, data is increasingly conceptualized as a form of capital:

Rather than data collection being seen as simply a way of producing and obtaining commodities that are somehow converted into monetary value, datafication takes shape as a political economic regime driven by the logic of perpetual (data) capital accumulation and circulation. (Sadowski, 2019: 2)

Hyperdatafication can be understood as situated within this paradigm. As the domain of human experience becomes increasingly “conquered” by technologies of datafication, the push for the continued accrual of data leads to a form of productive self-cannibalization wherein datafication itself is intentionally configured as a source material for datafication. Just as capitalism requires “outsides”—new frontiers for extraction and accumulation—for its continued operation, datafication is forced to create new frontiers of experience.

Conclusion

This article presents an exploratory theorization which aims to elucidate and contextualize transformations in the design and affordance structure of popular platforms. By engaging with the observed phenomenon of “social media-fication” (the notion that platforms which are not typically classified as social media are becoming increasingly “social media-like”) via a selection of three platform walkthroughs, we argue the constitutive dynamics of “social media-fication” involve the repurposing of “non-social media” apps with social and identity-oriented characteristics via the collection, repackaging, and resocialization of user behavioral data. As such, “social media-fication” can be understood as the implementation of infrastructure that captures the new forms of sociality, expressivity, and experience that emerge from the reintegration of previously captured data into social contexts—a kind of “datafication of datafication” which we term “hyperdatafication.”

Although this article presents an exploratory theorization of social media-fication and hyperdatafication, these concepts would benefit from further study to address several limitations. This research examines only three different kinds of platform case studies as exemplars of a broader social media landscape. Additional work could look at a broader array of different mobile apps or focus on a specific kind of app, such as music streaming platforms, further interrogating the blurred boundaries between “social media” and “non-social media” platforms. In addition, the walkthrough method only enabled us to analyze our own experiences of “everyday use.” Future research could use in-depth interviews or focus groups to examine other users’ affective experiences of social media-fication and hyperdatafication in more detail. Additional work should also further interrogate the economic implications of hyperdatafication to theorize how it supports platform capitalism in more detail as a form of social currency.

Ultimately, this study examines how tech companies further blur the boundaries of “social media” (social media-fication) to generate additional value by repurposing user behavioral data for expressive purposes, effectively generating a new user data source from the scraps of existing user data (hyperdatafication). Because the “non-social media” apps that we examined traffic in behaviors that are not primarily expressive, incorporating social media-like features and aesthetics enables them to breathe new life into everyday behavioral data and support enhanced user engagement with their platforms. This research adds nuance to existing discussions of datafication and affective capitalism by investigating how data and datafication become imbricated and intensified through affective experiences of social media-ness within an Internet dominated by affective capitalism. It is not just that digital platforms capture, analyze, and sell user “data doubles,” which, in our case studies, comprise the typically personal and mundane minutia of monetary transaction, location, movement, bodily, and listening data. Rather, these datafied identities are also transformed, through processes varying in their complexity, into aggregated social updates with capacities for self-expression, identity formation, and interaction. In turn, the social and expressive nature of hyperdatafication further entrenches datafication and capitalism into digital cultures that are conditioned to socialize, express, and identify themselves through diluted and platformized data aggregations. The relationship between hyperdatafication and the social normalization of datafication (and thus, the extensive network of platform surveillance which supports datafication) as a largely unquestioned element of digital social life is a topic well worth exploring in future research in order to draw out the broader implications of hyperdatafication.

This research presents theoretical extensions to critical data and platform studies that invite scholars to consider how long-standing processes of datafication and self-tracking can be iterated and often exploited in new ways. It also offers further evidence to support long-standing arguments that all platforms are social (e.g. Papacharissi, 2015). Considering social media-ness as an essence or social media-fication as a process enables scholars to analyze the power of social media as a sociotechnical construct rather than solely as a platform category. As digital platforms of all kinds continue to emerge, grow, recede, and collapse, we predict that capacities for theorizing platform hybridity and flexibility will become increasingly vital in a constantly expanding and temperamental apposphere.