1. Introduction

Platform capitalism has become the defining structure of the digital economy. As Srnicek argues, platforms do not merely act as intermediaries between producers and consumers but generate profit through the extraction of behavioral data, predictive analytics, and infrastructural control [1]. From ride-hailing services to social media and food delivery apps, platforms orchestrate and govern economic life by leveraging data from users, workers, and advertisers. Recent research further highlights the political and emotional dimensions of these platforms, noting how they have expanded into spheres of everyday life and self-expression. This paper analyzes platform capitalism from a sociological perspective, emphasizing how it restructures social interactions, transforms labor, and perpetuates social inequality. While theories such as Granovetter’s embeddedness, McLuhan’s electronic age, and Stuart Hall’s encoding/decoding model provide interpretive support, they are used to sharpen rather than distract from the central analysis of platform capitalism as a socio-economic system.

2. Platform capitalism and its social embeddedness

Srnicek identifies several key features of platform capitalism: data extraction, network effects, infrastructural ownership, and cross-subsidization [1]. These features allow platforms to dominate markets by centralizing control over digital space. Uber, for instance, owns none of the cars it dispatches, yet governs millions of rides daily through data and algorithmic management. Similarly, Meituan, the most used delivery app in China, regulates food delivery through AI systems that optimize time, cost, and worker output.

While such platforms present themselves as neutral technological innovations, they are deeply embedded within broader social and institutional contexts. Granovetter argues that “economic action is embedded in ongoing networks of personal relationships rather than being carried out by atomized actors,” emphasizing that economic behavior is shaped by networks, norms, and relationships [2]. Platforms operate within these networks: labor laws, urban infrastructures, and cultural expectations. Moreover, McLuhan's concept of the "electronic age" reveals how media technology breaks time and space, creating a highly interconnected environment in which platforms mediate work and self-expression [3].

Stuart Hall's encoding/decoding theory emphasizes how meaning is constructed through power [4]. Platforms not only carry user content; they also encode values into algorithmic design—prioritizing participation, speed, and profit. Users have different decoding methods for these cues, but the platform's default logic incentivizes visibility, optimization, and data contribution. Therefore, platforms have become powerful institutions that shape individuals' ways of working, socializing, and self-expression.

This embeddedness is also evident in the way platforms shape local urban environments and public services. For instance, food delivery platforms in Chinese cities adapt to the rhythms of local labor markets and traffic patterns, integrating themselves into urban life at the infrastructural level. In many areas, their operations have influenced zoning rules, courier lane usage, and even pedestrian flows. Rather than merely responding to pre-existing conditions, platforms actively mold the city’s logistical and temporal organization.

In addition, the embeddedness of platforms is not static—it is dynamic and negotiated. Government regulators, civil society, and user communities all play roles in shaping how platforms operate. In some regions, local authorities have implemented labor protections and data transparency requirements, creating a patchwork of regulations that platforms must navigate. This dialectical relationship between platforms and their environments underscores the need to analyze digital infrastructures not as universal entities, but rather as locally adapted and socially negotiated systems.

Thus, platform capitalism cannot be fully understood without appreciating its socio-cultural embeddedness. It extends beyond software and code, intertwining with everyday life, legal frameworks, and cultural norms, constantly being reshaped by and reshaping the societies in which it operates.

3. Labor and the datafication of the self

A central consequence of platform capitalism is the reconfiguration of labor and identity. Workers in the gig economy, like riders, drivers, content moderators, experience a shift from traditional employment to “datafied” labor. Their performance is tracked, evaluated, and often punished through opaque algorithms. As Huang notes in her ethnographic study of Meituan and other delivery platforms in China, couriers often feel “trapped in the system,” where algorithmic scheduling, performance scoring, and customer evaluations dictate every aspect of their labor [5].

Beyond algorithmic scheduling and surveillance, platform workers are increasingly subjected to a form of affective and emotional labor that remains largely invisible. Riders are expected to maintain “positive” communication styles, respond cheerfully to customer complaints, and avoid confrontation even when faced with unfair treatment or unsafe conditions. These emotional performances are not only unpaid but also enforced through customer ratings and algorithmic punishments, turning politeness and obedience into a form of extractable labor.

As Agger reminds us, “the self … is largely but perhaps not entirely, an outcome of social structure… To ignore all these external influences is to be a psychologist!” [6] This illustrates how platform capitalism shapes not only labor practices but also the very formation of selfhood through algorithmic and social mechanisms.

Moreover, the data generated from their behaviors, locations, and interactions is harvested without their having ownership of it or giving consent, reinforcing what some scholars term “data colonialism.” Therefore, gig workers are both laborers and data sources—embedded in a digital system that benefits from their physical movements, emotional reactions, and algorithm scores, often without transparency or accountability.

In this data-driven environment, labor is not only commodified but also deeply entwined with mechanisms of control, rendering the boundaries between work, identity, and surveillance increasingly blurred. Platform workers are no longer just service providers—they are also emotional performers, algorithmic subjects, and involuntary data sources. As their behavior, emotions, and digital traces are extracted and optimized for profit, their selves are segmented and reshaped to meet the platform's expectations. This not only exacerbates uncertainty but also heralds a deeper transformation: the rise of a “virtual self” shaped not by autonomy but by algorithmic visibility and behavioral compliance.

Furthermore, the concept of “data labor” complicates traditional understandings of productivity. Under platform capitalism, value is created not only through visible work like deliveries or content creation but also through the passive generation of behavioral data. Even actions such as browsing, liking, or pausing on a video contribute to a vast feedback loop used to refine algorithms and target advertising. This blurring of leisure and labor redefines who counts as a worker and what constitutes labor itself.

In this context, the body becomes a site of continuous optimization. Workers are nudged to improve their performance through gamified metrics—badges, ranks, and real-time feedback—that encourage self-monitoring and competition. While such mechanisms may appear empowering, they often impose subtle forms of coercion: failure to meet expectations can result in reduced visibility or loss of income.

Therefore, these systems normalize emotional self-regulation as part of the job. Maintaining a calm tone during disputes, masking exhaustion, and conforming to customer expectations become part of the algorithmically enforced repertoire of performance. The emotional cost of this invisibility are often unrecognized. Ultimately, the datafied self is not only shaped by external expectations but is gradually internalized by workers themselves, turning algorithmic logic into a lived habitus.

4. Inequality and algorithmic power

Platform capitalism exacerbates social inequality through mechanisms of algorithmic governance. Algorithms are not neutral; they reflect and reproduce existing biases. The job allocation system may penalize workers based on customer ratings that reflect racial, gender, or class biases. Visibility algorithms on social media may prioritize certain aesthetics or behaviors while marginalizing others.

Furthermore, platforms externalize risk and responsibility. Gig workers lack legal protection, stable income, and health benefits. Digital content creators face fatigue, unstable profits, and psychological pressure. Despite this, platforms continue to grow by leveraging these inequalities as profit drivers.

Surveillance is not merely a byproduct—it is foundational. As Zuboff argues, surveillance capitalism thrives by predicting and influencing behavior [7]. From targeted advertising to algorithmic scheduling, users are both subjects and resources. The feedback loop of platform interaction strengthens user consistency, suppresses dissent, and deepens platform dependence.

This growing dependence is especially stark among marginalized populations. Platforms often target those with limited employment options—migrant workers, students, the unemployed—offering “flexibility” that masks precarity. For many, platforms are not supplementary income streams but primary livelihoods, lacking the protections of formal employment. This dependence creates power imbalances where workers have minimal room to negotiate terms or resist exploitation.

Additionally, algorithmic opacity disproportionately affects those with lower digital literacy. Workers unaware of how platforms track and rank their behavior may struggle to understand sudden drops in visibility or pay. Without transparency, recourse, or explanation, they are left to guess what behaviors the system rewards or punishes. This asymmetry reinforces existing class and educational divides, thus granting further advantage to those with the resources to “game” the system.

On the user side, similar dynamics play out. Visibility algorithms on social media favour users who adhere to dominant aesthetic norms, often penalizing those from marginalized communities. The result is a digitally mediated reproduction of offline social hierarchies. In this sense, platform capitalism not only reflects social inequalities but actively re-embeds and intensifies them through automated and often invisible systems of control.

5. Conclusion

Platform capitalism is not just a market innovation; it is a deeply embedded social system that governs labor, identity, and everyday life through data. Although platforms are often seen as neutral technological infrastructures, they actively shape human behavior, reinforce social inequalities, and commodify even the most intimate aspects of the self. By embedding surveillance, algorithmic control, and emotional labor into digital routines, they extend economic logics into the social studies. Therefore, a sociological perspective is crucial—not only to reveal the power relations within seemingly efficient platforms, but also to imagine alternative digital futures rooted in transparency, equity, and human dignity. However, this paper is limited in geographic scope. Future research may explore regional variations in platform operations and potential platform alternatives.