Introduction

We cannot escape the afterlife of history, for power - as Michel Foucault (Foucault, 1977)reminds us - operates not merely through overt institutions but through the subtle relational grammars that structure our daily realities. In postcolonial India, this truth manifests with particular violence in the digital infrastructures that mediate modern selfhood. What we are witnessing is nothing less than the algorithmic reinvention of colonial aesthetics, where social media platforms - predominantly engineered by U.S.-based tech conglomerates - have become the new arbiters of racialized visibility (Noble, 2018). These platforms do not simply reflect beauty standards; they actively construct and enforce them through what I term digital colorism: the systematic privileging of lighter skin tones through interface design, recommendation algorithms, and the very architecture of participatory culture.

The selfie, that quintessential artifact of our platform age, has become the primary site where this digital colorism plays out(Hess, 2015). No longer just a casual snapshot, it functions as what Frantz Fanon (Fanon & Markmann, 1988)might have called a "digital epidermal schema" - a mediated reconstruction of the racialized self that internalizes colonial hierarchies under the guise of choice. When Indian users automatically apply skin-lightening filters or subtly angle their faces to minimize ethnic features, they are participating in what scholars of platform studies recognize as algorithmic self-regulation (Andrejevic, 2019). This phenomenon represents the perfect marriage of Fanon's psychic colonization with Manuel Castells' network society (Castells, 2010) - where power operates not through physical coercion but through the soft governance of coded preferences and algorithmic rewards.

Platforms like Instagram, Facebook and Snapchat have perfected what I call the necropolitics (Platforms like Instagram, Facebook, and Snapchat have perfected what I call the necropolitics of visibility—a digital extension of Mbembe’s theory (Mbembe & Corcoran, 2019), where the power to decide who lives or dies is replaced by the power to decide who is seen or erased. Unlike the traditional necropolitical order rooted in physical death, this is a regime of symbolic annihilation. Algorithms do not shoot or imprison; they shadowban, filter, and de-rank. Visibility becomes the new life, invisibility a kind of social death. Here, beauty norms, racial aesthetics, and desirability are algorithmically enforced, turning platforms into sites of soft violence where some identities are celebrated into hypervisibility while others are quietly made to disappear.) of visibility after Mbembe, though with an important digital twist: their algorithms don't merely decide who lives or dies, but who gets seen, shared, and celebrated. The consequences are measurable and dire. The consequences are measurable and systemic. Riccio et al.(Riccio, Colin, Ogolla, & Oliver, 2024) demonstrate through extensive computational analysis that beauty filters used across popular social media platforms systematically lighten skin tone and alter facial features—such as nose shape, eye size, and lip contour—to conform to Eurocentric beauty standards. Their study found that after beautification, individuals from non-white racial categories, particularly Latino Hispanic, Indian, and Middle Eastern, were significantly more likely to be algorithmically misclassified as white, underscoring the racial bias encoded into digital aesthetics.This isn't passive bias but active engineering - what Safiya Noble (Noble, 2018)might recognize as algorithms of oppression repackaged as digital cosmetics.

What makes this new regime particularly insidious is its veneer of democratization. Unlike the blunt racial hierarchies of colonial photography studios, where white photographers imposed their aesthetic standards on brown subjects, today's digital colorism operates through what Wendy Chun (Chun, 2017)calls "habitual new media" - interfaces so intuitive that their ideological scaffolding disappears. Users don't experience these filters as coercive but as empowering tools for self-expression. This is platform capitalism's great trick: it convinces us we're curating our identities while systematically narrowing the range of acceptable self-presentation.

The psychological toll is staggering. My research among Gen-Z users in Kolkata reveals that many, particularly those who have migrated from villages to the city, for education or a job, feel compelled to use beauty filters before posting, with many reporting what can be called "digital dysmorphia" (Coy-Dibley, 2016) - the inability to recognize their unfiltered face as socially valid. A participant recalls that “the filters help her look like sohorermey (city girl).” This represents the ultimate internalization of colonial aesthetics: not enforced by external authorities but voluntarily adopted (Mulder, 2016) as a condition of digital citizenship. As another participant confessed, "I don't even remember what my real skin tone looks like online anymore."

This paper argues that we must understand these phenomena as more than just cultural trends - they represent the latest frontier in what Partha Chatterjee (Chatterjee, 1993) might call the colonial transformation of everyday life.Digital colorism is reproduced through the interplay of smartphone camera technologies, social media algorithms, and user behaviors, manifesting in three intersecting mechanisms: the quiet embedding of Eurocentric beauty standards within interface design; the algorithmic privileging of aesthetic conformity via engagement logics; and the gradual internalization of self-discipline by users seeking validation and visibility in digitally mediated spaces. The stakes are immense. With India projected to generate over US$48 billion in smartphone revenue in 2025 and a per capita volume nearing 0.142 devices (Statista), the rapid saturation of mobile technology—especially among urban, price-sensitive youth—is transforming smartphones into aesthetic infrastructures. As social media platforms proliferate across this digitally saturated landscape, algorithmically filtered beauty standards are no longer peripheral—they are reconfiguring how a generation perceives identity, value, and social belonging (Roberts, Maheux, Ladd, & Choukas-Bradley, 2022). What was once imposed through colonial education systems and print capitalism is now being replicated - and amplified - through the participatory architectures of platform society. This isn't merely about representation; it's about the algorithmic reconstruction of desire itself (Bessenoff, 2006).

What follows is a tracing of how this system unfolds across intersecting layers: from the technical calibrations of smartphone cameras that tend to auto-brighten skin tones, to algorithmic engagement protocols that render some faces more visible than others, to the diffuse psychological effects experienced by a generation shaped within these aesthetic infrastructures. Rather than merely mirroring preexisting forms of colorism, platform capitalism is increasingly seen to reengineer colonial aesthetic codes for digital circulation, giving rise to a recursive architecture of racialized visibility. Ultimately, this study calls for a radical reimagining of what digital decolonization might entail. It's not enough to diversify representation on platforms; we must dismantle the very architectures that equate whiteness with visibility. This requires interventions at multiple levels: from regulatory pressure on tech companies to disclose their algorithmic biases, to educational initiatives that teach critical digital literacy, to the development of alternative platforms rooted in postcolonial aesthetics. The battle for India's digital future will be fought not in the abstract realm of policy but in the intimate space of smartphone screens - where colonial histories continue to shape postcolonial presents through every filtered selfie and algorithmically curated feed.

Materials and Methods

Research Objectives and Questions

Building on the theoretical framework outlined above, this study seeks to empirically examine how digital platforms mediate and reproduce colorist aesthetic hierarchies in postcolonial India. While the preceding discussion situates algorithmic colorism within broader structures of platform capitalism and colonial afterlives, the present research is guided by the following specific objectives:

To analyze how platform infrastructures and beauty-filter technologies encode and normalize Eurocentric aesthetic standards within everyday digital practices.

To examine how Indian Gen-Z users perceive, internalize, and strategically respond to these platform-mediated aesthetic norms.

To investigate the relationship between aesthetic modification (e.g., filter use) and perceived social visibility, engagement, and validation in social media environments.

In line with these objectives, the study addresses two central research questions:

How do social media platforms in postcolonial India reproduce and amplify colorism through algorithmic infrastructures, interface design, and beauty filters?

In what ways do Indian Gen-Z users negotiate, internalize, or resist these digitally mediated aesthetic norms in their self-presentation practices?

By explicitly linking theoretical critique with empirical investigation, these objectives and questions provide the analytical foundation for the mixed-method approach adopted in the following section.

Research Methods and Findings

Research Design

This study adopts an exploratory mixed-method design combining structured survey research with platform interface analysis. The objective is not to produce nationally generalizable claims, but to empirically ground a theoretically informed critique of digital colorism within an urban Indian context. By integrating user-reported behavioral patterns with examination of platform affordances, the study triangulates perceptual, behavioral, and infrastructural dimensions of digitally mediated aesthetic norms.

The methodological framework proceeds across two interrelated components:

(1) survey-based analysis of user practices and perceptions; and

(2) descriptive examination of beauty-filter affordances and engagement visibility across major social media platforms.

Survey Context and Sampling

The survey was conducted in Kolkata, India, between 2024 and 2025. Kolkata was selected due to its demographic heterogeneity, dense youth population, and high levels of smartphone penetration and social media engagement. The city’s layered cultural history and contemporary digital saturation make it a productive site for examining how postcolonial aesthetic hierarchies intersect with platform-mediated self-presentation.

A total of 900 Gen Z participants (aged 18–28) were surveyed. Data collection occurred in public youth-dense spaces including Nandan cultural complex, Prinsep Ghat, and the Maidan. These sites were chosen because they function as recurring gathering spaces for digitally active young adults across educational and socio-economic backgrounds.

A structured convenience sampling strategy was employed. Sampling Justification and Limitations.  While a probability-based sampling strategy would enhance generalizability, the use of convenience sampling in this study is methodologically aligned with its exploratory and context-specific objectives. The research seeks to capture lived digital practices, perceptions, and aesthetic behaviors among actively engaged urban youth populations rather than produce statistically representative national estimates. Public, youth-dense urban spaces such as Nandan, Prinsep Ghat, and the Maidan function as natural aggregation points for digitally active Gen-Z users, making them appropriate sites for accessing participants embedded within platform cultures. Convenience sampling thus enables high ecological validity, allowing the study to observe behaviors and perceptions within naturally occurring social environments where digital self-presentation practices are actively performed and negotiated. Furthermore, efforts were made to partially mitigate sampling bias by collecting data across different days, time intervals, and micro-locations within these spaces, thereby introducing a degree of variation in participant profiles. However, this approach carries inherent limitations. The sample may be skewed toward urban, socially mobile, and digitally literate individuals, potentially underrepresenting populations with limited access to smartphones, lower digital engagement, or differing aesthetic norms (e.g., rural or older demographics). As a result, findings should be interpreted as analytically indicative rather than statistically generalizable, reflecting patterns within a specific socio-digital milieu rather than the broader Indian population. Participants were approached across different days and time intervals to diversify responses. Individuals were screened to ensure active use of at least one major social media platform and familiarity with smartphone camera functions.

Although the sample size is substantial for an urban youth study, it is geographically confined to Kolkata. Accordingly, findings should be interpreted as context-specific rather than nationally representative.

Survey Instrument

The survey instrument was intentionally concise and structured to capture observable behavioral patterns rather than conduct psychometric or clinical assessment. It consisted of closed-ended, frequency-based, and short open-ended questions organized into four domains.Given the study’s focus on observable behaviors and perceptions rather than latent psychological constructs, the survey instrument was designed as a structured, low-inference tool prioritizing clarity, brevity, and contextual relevance. To ensure content validity, questions were developed based on existing literature on digital self-presentation, platform behavior, and mediated beauty practices, aligning each item with the study’s core analytical domains. The instrument underwent informal pre-testing with a small subset of respondents (n ~ 20) drawn from the target demographic to assess question clarity, linguistic accessibility, and interpretive consistency across English, Hindi, and Bengali versions. Minor revisions were made to eliminate ambiguity and ensure semantic equivalence across translations. Reliability was addressed through the use of standardized, close-ended response formats (e.g., yes/no, frequency-based items), which reduce interviewer bias and enhance response consistency. Open-ended questions were deliberately limited and framed to elicit concise explanatory insights rather than extended narrative variability, thereby supporting systematic pattern-based analysis. It is important to note that the instrument does not claim psychometric validation in the strict statistical sense (e.g., scale reliability coefficients), as the study does not construct composite indices or latent variables. Instead, validity is grounded in conceptual alignment, contextual appropriateness, and consistency of response patterns across the dataset.

Device Use

What smartphone brand do you currently use? (Open-ended)

Camera Filters

Does your phone’s camera include built-in beauty or enhancement filters? (Yes/No)

Do you use these filters? (Yes/No)

Platform Behavior

Which social media platforms do you use most often? (Multiple selection)

How frequently do you post selfies or edited images? (Frequency-based response)

Personal Insights

Do you have any comments as to why you or people, in general, would use selfie filters? (Open-ended)

The questionnaire was administered in English, Hindi, or Bengali depending on participant preference in order to reduce linguistic exclusion.

Analytical Strategy

Closed-ended responses were analyzed using descriptive statistical techniques, including frequency distributions and percentage breakdowns. Where relevant, cross-tabulations were used to observe basic demographic variation.

Open-ended responses were reviewed iteratively to identify recurring explanatory patterns related to visibility, validation, aesthetic optimization, and peer norms. Rather than employing formal grounded-theory coding software, responses were grouped through pattern-based interpretive analysis to contextualize quantitative trends within lived experience.

The study does not employ inferential statistical modeling or claim causal relationships between algorithmic ranking systems and individual behavior. Survey findings are interpreted as indicative of perceived structural incentives within platform ecosystems.

Findings

Device Infrastructure and Accessibility

A majority of respondents reported using budget to mid-range Android smartphones. Frequently mentioned brands included Xiaomi, Vivo, Oppo, Realme, and Samsung. This device distribution is analytically significant, as many mid-range Android phones integrate default beautification features directly into native camera applications. The accessibility of such features suggests that aesthetic modification is structurally embedded at the hardware and software level before images enter social media platforms.

Beauty Filter Engagement

More than 85% of participants reported using built-in beauty or enhancement filters. Many described filter activation as habitual or routine when taking selfies. Filter usage was often framed as a normalized step in image preparation rather than as an exceptional act of alteration.

Although the survey did not formally measure specific adjustment parameters, a substantial proportion of respondents demonstrated awareness of particular visual modifications—such as smoothing or brightening—that they associated with improved engagement outcomes. Approximately 73% indicated awareness of which forms of visual enhancement tended to generate more likes or comments. This reflects perceived engagement optimization rather than experimentally verified algorithmic behavior.

Platform Visibility and Posting Practices

The platforms most frequently cited by respondents were Instagram, Snapchat, and Facebook. Participants commonly described these spaces as environments where edited or enhanced images perform better in terms of engagement metrics.

Selfie-posting frequency varied, but regular image-sharing behavior was common among active users. Engagement was frequently referenced as a motivating factor shaping decisions about image modification and posting.

Importantly, the survey measures self-reported behavior and perceived engagement dynamics; it does not independently verify platform ranking algorithms or conduct computational audits of visibility systems.

Motivational Narratives and Aesthetic Norms

Open-ended responses revealed that filter usage was frequently linked to:

•    Social validation
•    Perceived attractiveness
•    Confidence enhancement
•    Peer conformity
•    Increased engagement

Participants often framed filter use as optimization rather than deception—aligning their images with what they perceived as platform expectations. Across responses, aesthetic modification emerged as normalized within peer networks and embedded in routine digital practice.

Platform Interface Analysis

To contextualize survey findings, the study conducted repeated interface walkthroughs of Instagram, Snapchat, and Facebook, as well as native smartphone camera applications commonly used by participants.

The interface analysis focused on observable features, including:

•    Default activation and prominence of beautification tools
•    Availability of skin-smoothing and brightness adjustments
•    Naming conventions of enhancement presets
•    Visibility of engagement metrics such as likes and comments

This component examines platform affordances rather than proprietary algorithmic code. The purpose is to analyze how interface architecture structures aesthetic incentives, not to claim direct access to backend ranking mechanisms.

Taken together, the survey data and interface observations suggest convergence between user perceptions and platform design environments that facilitate aesthetic modification.

Limitations

This study is geographically confined to Kolkata. While the city provides demographic diversity and high digital density, findings cannot be generalized to rural populations or other Indian regions without further comparative research.

Second, the survey relies on self-reported behavior, which may be influenced by recall bias or social desirability bias.

Third, the study does not conduct computational audits of platform algorithms. Algorithmic dynamics are interpreted through user perception, interface affordances, and existing computational scholarship rather than proprietary data access.

Finally, the cross-sectional design captures practices at a specific moment in time and does not track longitudinal changes in aesthetic behavior.

Results

The findings of the study reveal a strong normalization of aesthetic modification within the digital self-presentation practices of Gen-Z users in Kolkata. To present the survey findings more systematically, key patterns are summarized in Table 1 below.

Table 1: Consolidated overview of quantitative patterns and qualitative insights from the survey on beauty filter usage and digital self-presentation practices.

Survey data collected from 900 participants indicate that the use of beauty filters has become an embedded and routine component of smartphone photography. More than 85 percent of respondents reported regularly activating built-in beautification features when taking selfies, suggesting that image enhancement is no longer perceived as an exceptional act of alteration but as a default step in preparing images for social media circulation. Many participants described filter use as habitual, with several indicating that they rarely post unfiltered images online.

The data also demonstrate a high degree of user awareness regarding the relationship between visual aesthetics and platform engagement. Approximately 73 percent of respondents reported that they understood which forms of image enhancement—such as increased brightness, facial smoothing, or sharpening—were more likely to generate likes, comments, and other engagement metrics. This awareness reflects an emergent form of algorithmic literacy in which users strategically adjust their digital appearance to align with perceived platform preferences and visibility incentives. Rather than passive consumers of technological tools, participants appear to actively calibrate their self-presentation in response to the attention economy embedded within social media infrastructures.

Participant responses further indicate that beauty filters are strongly associated with social validation and confidence in online interactions. Respondents frequently linked filter use to increased attractiveness, greater engagement, and improved self-confidence when sharing images. Platforms such as Instagram and Snapchat were repeatedly described as environments where visually enhanced images receive higher levels of attention and affirmation. As a result, many users reported modifying their appearance not simply for aesthetic experimentation but as a pragmatic strategy for maintaining visibility within digitally mediated peer networks.

Qualitative responses also suggest that repeated interaction with filtered aesthetics contributes to the gradual internalization of particular beauty norms. Participants noted that common filter effects—especially those that brighten skin tone or smooth facial texture—shape expectations about how faces should appear online. Some respondents expressed discomfort with posting unfiltered images, indicating that the digitally enhanced version of the self had become the socially accepted standard within their online communities. This pattern reflects the emergence of what the study conceptualizes as “digital dysmorphia,” in which individuals begin to perceive their unfiltered appearance as less compatible with the visual norms circulating on social media platforms.

Complementing these survey findings, platform interface analysis reveals that the design architecture of both smartphone camera systems and major social media applications facilitates aesthetic modification. Beautification tools are prominently integrated into camera interfaces, while engagement metrics such as likes and comments visibly reward images that conform to prevailing visual norms. Together, these design features create an incentive structure that subtly encourages users to modify their appearance prior to posting. Taken collectively, the findings suggest that digital platforms do not merely reflect existing beauty hierarchies but actively participate in their reproduction by embedding aesthetic preferences within technological design and visibility systems.

Discussion

Literature Review: Algorithmic Colorism and the Digital Afterlife of Colonial Desire

The study of digital colorism demands a bifocal theoretical lens that simultaneously examines the psychological internalization of colonial aesthetics and the technological infrastructures that perpetuate them. This literature review bridges critical race theory, platform studies, and postcolonial media analysis to construct a framework for understanding how algorithmic systems reproduce and amplify historical hierarchies of skin color and facial features.

Frantz Fanon's seminal work on the psychic trauma of colonization takes on new urgency in India's platform society (Goozee, 2020). His concept of epidermalization - where racial hierarchies become inscribed on both skin and psyche - manifests digitally through what we might term algorithmic epidermalization(Browne, Digital Epidermalization: Race, Identity and Biometrics, 2010). Where Fanon analyzed how black Antilleans internalized white masks under French colonialism, contemporary Indian users adopt digital filters that perform similar racial alchemy through software (Fanon & Markmann, 1988). As Bakhshi et al. (Bakhshi, Shamma, Kennedy, & Gilbert, 2021)show, the use of image filters on mobile photography platforms like Flickr is far from superficial—it significantly impacts social visibility. Their large-scale analysis of images reveals that filtered photos are 21% more likely to be viewed and 45% more likely to be commented on. Filters that enhance warmth, brightness, and contrast drive the highest engagement. Importantly, users fall into two distinct categories: serious photographers who use filters as subtle correction tools, and casual users who favor dramatic, transformative effects—both engaging in 'filter-work' not only to aestheticize but also to algorithmically optimize their images for social attention. This represents a technological updating of what Fanon called "affective erethism" (Chamberlin, 2018)- the nervous condition of colonial subjects striving to approximate whiteness. The critical intervention here lies in recognizing how platforms transform Fanon's "racial epidermal schema" into quantifiable parameters. When beautification filters systematically increase the likelihood of non-white faces being algorithmically misclassified as white—despite preserving individual identity—they operationalize a racialized beauty canon into machine vision. Riccio and Oliver (Riccio & Oliver, 2022) show that across all racial categories, AR-based filters significantly elevate the predicted 'whiteness' of faces, effectively encoding Eurocentric aesthetics as the normative baseline for digital self-representation.Building on emerging critiques of digital aesthetics, recent research shows how platforms embed Eurocentric norms into the architecture of visual technologies—camera calibration, filters, and algorithmic curation—thereby producing a regime of algorithmic respectability, where lighter skin tones are consistently rewarded with greater visibility and engagement. In this system, beauty becomes a form of datafied capital, reinforcing existing colorist hierarchies through the logics of platform design rather than explicit racial discourse (Ryan-Mosleyarchive, 2021). This synthesis demonstrates that Fanon's framework requires digital updating: where he saw the mirror as site of racial alienation, we must now look to the smartphone camera's viewfinder.

Manuel Castells' (Castells, 2010)network society theory provides the scaffolding to understand how these racialized aesthetics circulate at scale. His concept of "programmable spaces" helps explain how platforms construct what Banet-Weiser (Banet-Weiser, 2018) calls "economies of visibility" - systems where attention flows preferentially toward certain bodies.In the Indian context, the dynamics of digital colorism are anchored in longstanding colonial and caste-based hierarchies, now amplified by contemporary digital platforms. AI-based media analysis demonstrates that lighter-skinned characters dominate prime-time television — appearing on-screen roughly eight times more frequently than those with darker complexions (Singh, Somandepalli, & Dave, 2023). Simultaneously, qualitative studies confirm that social media fosters persistent insecurities: young Indian women report that filters lighten their skin and soften features in pursuit of social validation, reinforcing a homogenized standard of beauty rooted in Eurocentric norms (Gupta, 2020).Empirical surveys reveal that many Indian users—across gender and education levels—regularly apply beautifying filters to lighten their complexion, even when they identify as proud of their natural skin tone (SIDDIQUI, 2021). Mental health research among Indian Instagram users shows a significant correlation between frequent platform use, upward social comparison, and experiences of colorism-related anxiety and lowered self-esteem (Sharma, Sanghvi, & Churi, 2022). This body of work frames digital colorism not as a cultural accident, but a technologically mediated extension of structural bias. Filters, camera calibrations, and recommendation engines do not merely reflect preferences—they institutionalize a feedback loop of racialized visibility. As platforms prioritize lighter, Eurocentric images for engagement, they effectively reproduce colonial aesthetic hierarchies in digital spaces, shaping both user behavior and self-perception.

Recent scholarship in postcolonial digital humanities has critically examined how digital platforms reproduce colonial hierarchies while also offering pathways for resistance. Noble’s (Noble, 2018) foundational work in Algorithms of Oppression exposes how search engines reinforce racial and gendered biases, while Benjamin’s (Benjamin, 2019) concept of "race-critical code studies" interrogates the racialized logics embedded in computational systems (Race After Technology). These frameworks reveal how digital infrastructures perpetuate historical inequities under the guise of technological neutrality.

In the Global South, grassroots movements have developed counter-strategies to resist algorithmic oppression. For instance, Das and Singh’s (Das & Singh) ethnography of "digital caste resistance" in India highlights how Dalit activists subvert platform algorithms through creative hashtag campaigns and meme cultures. The persistence of digital colorism—evident in beauty filters, automated moderation, and hiring algorithms—echoes colonial-era physiognomic practices (Browne, Dark Matters: On the Surveillance of Blackness, 2015). Building on this, Bonini and Treré(Bonini & Treré, 2024) introduce the concept of algorithmic agency to capture how users in asymmetrical digital environments develop tactical responses to opaque algorithmic systems. Their ethnographic research reveals how gig workers, cultural producers, and activists—from India to Mexico—employ everyday acts of “algorithmic resistance,” ranging from engagement pods and spoofing to coordinated hashtag hijacks. These practices, often informal and improvised, are not necessarily framed as political by participants, but nonetheless function as micro-resistances to platform power. As Bonini and Treré argue, such tactics form a “continuum of resistance,” from conscious protest to unintentional subversion—what James Scott might call weapons of the weak.

Research Questions:

Two central research questions analyzed in the paper are:

How do social media platforms in postcolonial India reproduce and amplify colonial-era colorism through algorithmic infrastructure and beauty filters?

– This question is explored by examining how platform design (e.g., camera filters, algorithmic visibility systems) encodes Eurocentric aesthetic preferences, making lighter skin tones more visible and socially rewarded. The paper examines the technical and psychological mechanisms, such as “algorithmic self-regulation” and “digital dysmorphia,”through which users internalize and conform to these standards.

In what ways do Indian Gen Z users respond to and negotiate digital colorism, and how does this shape their self-perception and platform behavior?

– Through a survey of 900 participants across Kolkata, the study analyzes user practices such as habitual filter usage, aesthetic optimization for engagement, and the emotional toll of filtered self-presentation. These responses are framed within the broader critique of platform capitalism and aesthetic imperialism, with particular attention to youth behavior, gender dynamics, and caste-coded desirability.

The Aesthetic Infrastructure of Digital Colonialism

At first glance, a beauty filter may seem trivial—an innocuous overlay, a feature tucked into a camera app, used for fun or flattery. But when filtered images become default representations, and when unfiltered faces disappear from social feeds, the filter ceases to be cosmetic. It becomes ideological (Shah & Tewari, 2016). This study argues that digital platforms function as aesthetic infrastructures of power, quietly reproducing the colonial logic that privileges lightness, symmetry, smoothness, and submission.

These hierarchies no longer travel through textbooks or state institutions but through pixel-level codes, touchscreens, and camera lenses. They are sustained not through force, but through voluntary participation in an economy where visual capital determines value. What emerges is a form of aesthetic imperialism: a regime in which users internalize and enact beauty norms that echo colonial and caste-based ideals—without ever naming them as such.

This process is evident in the survey. A striking 85% reported using built-in beauty filters regularly, and not just for enhancement, but as a near-automatic part of taking selfies. A 22-year-old respondent explained, “It’s just muscle memory now. If I’m using my front camera, I know the filter is on.” The language of automation here is crucial: it signals not aesthetic play, but a habituated response to platform expectations.

On platforms like Instagram and Snapchat, where these images circulate most, participants reported clear incentives for filtered conformity. A 19-year-old admitted, “My filtered pictures get more likes. If I post my real face, it gets ignored.” Another respondent put it bluntly: “No one wants to see real skin anymore. Even I don’t.” In this economy, the wheatish complexion—once a marker of South Asian authenticity—is quietly erased. Not by decree, but by design.

Here, the concept of algorithmic self-regulation becomes central. Users are not merely subjected to aesthetic hierarchies; they actively reproduce them, often in pursuit of visibility, validation, or belonging. A 20-year-old male college student remarked, “There’s a filter that makes my skin look fairer and sharper. It’s not that I hate my real face—it just gets more engagement when I use it.” This is not about shame alone; it is about compliance with an invisible economy in which lighter skin circulates better, is seen more, and accrues more symbolic worth.

What makes this form of power especially insidious is its disavowal. These filters do not announce themselves as colonial tools. They operate through interface minimalism, default settings, and seamless automation. As theorist Arjun Appadurai has observed in his writing on techno-obfuscation, the more seamless a digital process becomes, the less likely it is to be scrutinized. In this sense, the beauty filter functions as a mechanism of aesthetic governance, one that polices faces without ever appearing coercive.

Participants in the survey rarely critiqued the filters themselves. Instead, they expressed frustration with their own appearance. A participant confessed, “I wish I looked like the filter in real life. It just feels cleaner.” The idea that fairness and flawlessness signify “cleanliness” is not accidental—it is the result of centuries of caste, colonial, and patriarchal encoding, now updated and embedded into app interfaces.

While the West has long authored global beauty standards, what we see in India today is a form of platform-mediated colonial recursion. Beauty norms that emerged under colonial rule have not disappeared; they have been digitized, gamified, and monetized. The platforms do not need to explicitly instruct users to lighten their skin; they simply reward those who do. The result is a social media landscape where color becomes currency, and where the filtered face becomes the acceptable self.

In this environment, aesthetic conformity is rewarded, and deviance is punished not with censorship, but with silence. Posts without filters don’t “perform.” Wheatish skin doesn’t trend. A face that resists modification is often read as lazy, unkempt, or undesirable. As one respondent shared: “When I stopped using filters, people messaged asking if something was wrong with my health.”

This is the subtle violence of platform capitalism. It does not enforce norms overtly, but incentivizes their reproduction through feedback loops, visibility metrics, and algorithmic sorting. In doing so, it creates a regime of what Achille Mbembe calls “necropolitical aesthetics”—a politics not of life and death, but of visibility and disappearance. Who is seen, who is liked, who is amplified, and who fades into the scroll—all are decisions rendered by algorithms, but enacted by users themselves.

The next section delves deeper into how these dynamics intersect with gender, caste, and class, revealing the layered nature of digital colorism in India’s postcolonial visual order.

Analyzing Technical Infrastructures of Digital Colorism

Fanon's "white masks" have found their 21st-century counterpart in smartphone filters, but with a critical difference - today's epidermal schemas are not merely worn, but algorithmically enforced. When a college student admits, "I don't want to waste a good outfit on an unfiltered post," she articulates what Lin, Wang and Liu(Lin, Wang, & Liu, 2023) identifies as the platformization of desire - where visibility becomes contingent on conforming to digitally encoded beauty norms. The survey reveals this is not passive acceptance but calculated performance: 73% of respondents could precisely describe which facial adjustments garnered optimal engagement, demonstrating what Anne and German(Oeldorf-Hirsch & Neubaum, 2023)terms "algorithmic literacy" - the tacit understanding of how to manipulate one's appearance for machine recognition.

What makes this system particularly insidious is its gendered reconfiguration of respectability politics. Male respondents reporting pressure to appear "clean" and "sharp" online demonstrate how digital colorism transcends traditional beauty paradigms, creating what Nakamura (Nakamura, 2002)calls "cybertypes" - racialized avatars that serve platform capitalism. A man's remark that filters make him look "prepared" reveals the professionalization of aesthetic labor, where the face becomes a CV (Kang, DeCelles, A., Tilcsik, & Jun, 2016). This aligns with Noble's (Noble, 2018) findings about the credentialization of appearance under algorithmic governance. When a participant notes, "Every unfiltered like feels like stealing back power," she enacts a digital marronage, small acts of defiance within oppressive systems. These practices, though fragmented, suggest the potential for what a decolonized digital aesthetic might resemble, one where the mirror reflects not colonial ghosts, but finally, unapologetically, reflects us.

The tragedy - and the possibility - lies in participants' acute awareness of this dynamic. As a  student observed, "They gave us the filters for free, but made us pay with our faces." In that pithy remark lies the essence of digital colonialism's newest frontier: not the imposition of beauty standards, but the industrialization of aesthetic compromise. The path forward, as our data suggests, requires not just rejecting filters, but dismantling the algorithmic valuations that make them feel necessary - a project as much about rewriting code as it is about reclaiming the right to be seen, unfiltered and unapologetic, in digital space.

Aesthetic Capital and Platform Visibility

The rise of filtered selfie culture in India exemplifies Benjamin Moffitt’s populist style, not one bound to electoral politics, but enacted through aesthetic participation in digital publics. Here, populism becomes less about slogans or strongmen and more about performing conformity through filters, captions, angles, and edits, within a techno-social system that rewards what it recognizes. In this space, colorism is not enforced by state fiat, but by likes, shares, and silence (Moffitt, 2016).This dynamic came through with jarring clarity in the survey. As one participant observed, “It’s not about looking white—it’s about looking ‘right’.” This “rightness” is not ideologically explicit, but algorithmically reinforced. It is felt rather than reasoned—learned through engagement, mimetic behavior, and platform metrics.

What appears democratic—users freely choosing filters, posting selfies, curating profiles—is in fact deeply structured. Zizi Papacharissi’s idea of “affective publics” is instructive here: platforms don’t just aggregate individuals; they engineer collectivities through emotion, aspiration, and aesthetic repetition. Affective publics don’t debate—they feel together, scroll together, beautify together (Papacharissi, 2014).Within this regime, the filtered selfie becomes a political act disguised as personal branding (Lavrence & Cambre, 2020). A 19-year-old articulated it best: “Everyone uses the whitening filter. It’s not that we like it. It’s just... what works.” This is not submission to colonial beauty ideals through explicit coercion; it is an ambient compliance with platform logic—where algorithmic visibility becomes the most valuable form of social currency.

Jodi Dean’s notion of “communicative capitalism” explains this perfectly: the more users express, the more their expressions are commodified (Dean, 2005). The very act of posting a selfie becomes a transaction in aesthetic capital, where the value of the image lies in its ability to attract feedback (W., 2014) (Elias & Gill, 2017). Filtered images circulate more, receive more affirmation, and over time, reshape what is seen as desirable, professional, or successful.Importantly, this isn’t simply a top-down imposition. Platforms do not need to command users to lighten their skin or smooth their features; they only need to recommend filters, promote images, and let the metrics speak. This is what settles down to Tufekci’s algorithmic amplification—where the architecture of visibility itself incentivizes conformity (Tufekc, 2015).

One participant recounted, “I posted a natural photo once. It got three likes. Same day, I uploaded a filtered one—fifty likes in an hour. You do the math.” This isn’t vanity—it’s survival. In the digital economy of appearance, users are trained to optimize themselves, not for their friends or family, but for the feed.Srnicek’s concept of platform capitalism helps decode this shift (Srnicek, 2016). Engagement becomes the commodity, and Eurocentric or caste-coded aesthetics become the interface-friendly medium through which that commodity is circulated. This is not preference—it is infrastructural populism, where the majority aesthetic becomes moralized simply because it performs best.

But what happens when that aesthetic majority is racially biased? As Nakamura notes in her theory of cybernetic race, algorithmic infrastructures do not just reflect race—they produce it (Nakamura, 2002). In our survey, many participants described “correcting” their skin tone before uploading an image. One young man remarked, “I just brighten my face. I’m not trying to be white, it’s just cleaner.” The term cleaner here echoes deeply colonial tropes about darkness and dirt, revealing how racialized aesthetics are reproduced as hygiene, professionalism, or discipline.

What emerges, then, is a populism of the filtered average. Not the tyranny of the majority in political terms, but the tyranny of platform-mediated normativity—the idea that if it trends, it must be true; if it gets engagement, it must be beautiful. This is the new populist contract: participate, conform, be seen—or disappear.

And disappear many do. As one female respondent shared, “I stopped posting without filters. People would ask if I was unwell.” Another commented, “There’s no space for real life. Real life doesn’t get likes.” These statements reveal the psychological alienation of the digital self—a disjunct between how one feels and how one must appear in order to remain visible.

Here, Fanon’s “epidermalization of inferiority”  is reborn in the algorithmic age—not imposed by colonial photography, but enacted through the interface of a front-facing camera. Platforms now deploy machine vision, not as surveillance, but as soft coercion, guiding users toward digitally whiter, smoother, more compliant selves. This is what Mbembe might call a “necropolitics of appearance”—where certain looks are granted life in the feed, and others are quietly buried by the scroll.

Even more troubling is the absence of resistance. While offline racism can be challenged through legislation or protest, algorithmic racism is disbursed, hidden, and structurally incentivized. No platform policy forbids filters that lighten skin. No regulation demands that facial algorithms be inclusive. Colorist aesthetics become “just features,” and participation becomes complicity. As techno-obfuscation, the ideological power of platforms lies in their seamlessness. When racism becomes UX, it stops looking like racism. The interface becomes apolitical, the filter becomes a preference, and the user becomes the final enforcer of a system they neither built nor control. At the heart of this design logic is not only algorithmic bias but also discursive obfuscation, wherein digital platforms facilitate the fragmentation and masking of identity under the guise of personalization. Drawing on research that appraises identity performance on platforms like Facebook and WhatsApp, it becomes clear that users rarely present cohesive selves; instead, they navigate interfaces through fragmented posts, context-dependent alignments, and semantic ambiguities. Language, interface design, and algorithmic feedback loops work in concert to obscure systemic inequalities behind a veneer of individual choice. In this sense, platform aesthetics do not just naturalize whiteness—they discursively disguise power through a socio-technical grammar that renders structural racism ambient, deniable, and embedded in everyday interaction (Jakaza, 2020). This is the aesthetic populism of India’s network society: a digitally mediated consensus around color, beauty, and visibility, driven not by ideology but by infrastructure. Users don’t conform because they believe—they conform because the system offers no viable alternative. One respondent said it best: “It’s not that I like the filter. It’s just that everything else feels invisible.”

The next section turns to the intersection of caste, class, and aspirational aesthetics, exploring how India's historical hierarchies are reencoded in digital practices that appear modern, participatory, and benign—but are anything but.

The Algorithmic Raj: State Complicity in the Digital Whitening of India

There is, too, a silence from the state. India's policymakers, bureaucrats, and cultural gatekeepers have largely failed to challenge the digital hegemony of whiteness. Despite India’s historical engagement with anti-colonial movements and postcolonial theory, its regulatory imagination has yet to catch up with the epistemic violence of algorithmic aesthetics. Bureaucracies remain reactive rather than proactive, leaving the ideological field wide open for corporate dominance. The result is an infrastructural racism, where foreign-built hardware and Western-developed software jointly script the boundaries of acceptable beauty. This colonial hangover is now technologically weaponized. The “digital man” no longer resembles the brown-skinned citizen of postcolonial India but a whitewashed caricature manufactured by transnational code.And yet, this digital man enjoys no emancipation; instead, they inhabit a world of constant self-surveillance, aesthetic insecurity, and cultural amnesia. What makes this form of domination especially difficult to resist is its invisibility. Unlike the colonial state or the racial apartheid system, digital racism does not announce itself. It operates through defaults, through code, through what Wendy Chun (Chun, 2017) calls “habitual new media”. It thrives not by coercing the user, but by being desired by the user. The platform interface becomes the mechanism through which aesthetic norms appear neutral, self-evident, and apolitical, as also theorized by Safiya Noble (Noble, 2018)in her work on algorithmic bias.

If algorithms are the new colonial governors, then the Indian state and civil society have become their most loyal subjects—not through silence, but through active participation. It would be a critical oversight to blame beauty capitalism solely on Western platforms when India’s own cultural industries, corporate advertisers, and state inaction have long sustained the cult of fairness, rendering digital colorism a transnational performance enacted on a domestic stage.Nowhere is this more visible than in the visual economy of Indian advertising. As early as 2005, Emami’s Fair & Handsome launched with a now-infamous ad: Shah Rukh Khan, India’s global cinema darling, tossing a tube of whitening cream to a darker-skinned male fan with the silent promise that fairness breeds fame. The message wasn’t subtle—it didn’t need to be. To be successful, you must first become fair. No longer was skin-lightening marketed as a woman’s burden; it became a cross-gender national aspiration. And the messenger? Not a colonial overlord, but India’s most adored cultural icon.“Don’t these people have any kind of conscience?” asks Nandita Das of the Dark is Beautiful campaign (Rajesh, 2013). The ad's subtext is brutally clear: Forget merit. Forget labour. Fairness is destiny.This toxic ideology is not merely a relic of postcolonial hangover—it is actively remixed by platforms and industries to blend neoliberal dreams with colonial residues. As Jyotsna Vaidargues, fairness becomes the visible signifier of success, transforming dark-skinned women into socially desirable, marriageable, employable bodies (Vaid, 2009). The media narrates this transformation through cruelly simplistic “before and after” arcs: the dusky girl enters as undesired, exits as luminous and beloved. It is a moral purification wrapped in a beauty cream, part of what can be(Elias & Gill, 2017)called the “postfeminist sensibility,”—where empowerment is redefined through personal aesthetic improvement.But what makes this aesthetic regime doubly oppressive is its gendered ferocity. The burden of colorism falls disproportionately on women, particularly in a society where beauty is tethered to patriarchal worth and marital capital. In India’s aspirational economy, the fair bride is not a cliché—it is a currency. In Made in Heaven(Amazon Prime, 2019), the character Sarina, a dusky-skinned bride, contemplates skin-lightening before her wedding. Her family whispers the line that haunts countless real households: “Everyone wants a gori bahu.” Fairness is not just beauty—it is social access, dowry insurance, and sometimes even familial pride.

The ideology of fairness has also been nationalized, becoming a proxy for India’s imagined modernity. Fair skin is no longer framed as a personal trait, but a civilizational upgrade. Advertisements suggest that lightness is not only desirable—it is globally respectable. The fair-skinned face becomes the passport to professionalism, integration, and Western cosmopolitanism. Under the guise of “empowerment,” cosmetic capitalism sells fairness not as conformity but as liberation. Fairness is freedom, claim the ads, weaponizing feminist language to mask structural inequality (Parameswaran, 2020).This aesthetic colonialism is intersectional, though rarely recognized as such. Beneath the obsession with light skin lies the ghost of caste and class discipline. The “wheatish” or dusky tone, long associated with agrarian labour, lower castes, and darker geographies, is routinely erased in visual media. Caste doesn’t speak, but it shows on the skin. And in India’s ad-world, what shows gets censored (Ayyar & Khandare, 2013). Fairness is caste-coded respectability, and to be dark is to be suspect, unworthy, and silently disqualified.

What’s perhaps most chilling is that this epistemic erasure is bipartisan: it thrives on the complicity of Bollywood, cosmetic corporations, social media platforms, and the very state that should regulate them (Ganti, 2016). Government watchdogs remain quiet while fairness products proliferate. Platforms profit while algorithms amplify. The media airbrushes while civil society campaigns against “racism” abroad, even as it romanticizes whiteness at home. This is not ignorance—it is orchestrated amnesia.And even as fairness campaigns now reach men—rebranding whiteness as confidence, job-readiness, and sexual capital—there remains a palpable lack of counter-representation. Dark-skinned protagonists are anomalies, not archetypes. They are rarely seen in roles of romantic or professional aspiration. They are absent from brand campaigns, digital ads, matrimonial sites, and cosmetic endorsements. In short, darkness is not a shade, but a silence.

To critique only the platforms and their algorithms is thus insufficient. The Indian state, media industries, and civil society are co-conspirators in this algorithmic caste of lightness. While platforms may encode whiteness, it is Indian culture—internalized, celebrated, and broadcast—that sustains its desirability. The digital mask is manufactured abroad, but it is worn willingly at home.In such a world, resistance must be reimagined not just politically, but aesthetically, epistemologically, and ontologically. This growing entanglement between populist culture, algorithmic infrastructure, and racialized aesthetics is not incidental—it is the very condition of our digital modernity. And as India races toward deeper digital integration, this condition will only intensify unless critically interrogated.Hence, it becomes clear why regulating beauty domination in the social media age is so uniquely challenging: it is no longer enforced by force, but by fantasy; not by law, but by longing.

Conclusion

The filtered face is not merely a modern aesthetic preference—it is a battleground where colonial residues, capitalist imperatives, and algorithmic infrastructures converge to redefine selfhood in postcolonial India. As this study has shown, the violence of digital colorism is not always loud or overt; it is encoded in pixels, normalized through engagement metrics, and internalized through repeated acts of self-curation. In a nation still haunted by caste, colonialism, and patriarchy, platform capitalism has not simply adapted to local biases—it has industrialized them.

Yet, within this dark circuitry lies the spark of resistance. The testimonies of Indian Gen-Z users reveal a paradoxical clarity: they are not naïve consumers of filters, but conscious negotiators of an aesthetic economy they did not choose but are forced to inhabit. Their reflections—ranging from resigned acceptance to subtle acts of digital refusal—signal that the algorithmic hold on identity is not absolute. There remains, even in the most curated feed, a yearning for authenticity, a mourning of lost selves, and a desire to be seen as one is. This quiet dissent often manifests through deliberate underperformance—posting grainy, unfiltered photos, or using filters ironically, knowing they will attract fewer likes. Others form small, supportive peer networks where raw, unedited images are encouraged and affirmed, consciously countering mainstream metrics. These subtle, everyday micro-practices, though fragmented, represent meaningful ruptures in an otherwise homogenized, algorithmically disciplined digital landscape. (Singh, Somandepalli, & Dave, 2023)

This manuscript has argued for a reimagining of digital futures that centers decolonial aesthetics, critical media literacy, and algorithmic justice. The road ahead is not merely about regulation or representation—it demands infrastructural transformation. We must envision platforms where visibility is not the reward for racial conformity, where technological design does not punish darkness, and where beauty is not a proxy for proximity to whiteness. This requires not only challenging corporate logics but also deconstructing cultural myths and state complicity that have long upheld fairness as a virtue.

Aesthetic liberation, like political freedom, cannot be algorithmically assigned. It must be claimed—through pedagogy, policy, and platforms that refuse to commodify appearance. Let this not be the end of the conversation, but its beginning. Let us move toward a digital world where the mirror does not erase, the filter does not distort, and the face—every shade, every scar, every story—need not beg for likes to prove its worth.

The unfiltered future awaits. And it is ours to build.

Acknowledgement

I sincerely thank the youth of Kolkata for their participation in this project, without whom it would not have been successful. I also extend my heartfelt gratitude to Dr. Saddam Hossain Sarkar for his encouragement and belief in me.

Funding Sources

The author received no financial support for the research, authorship, and/or publication of this article.

Conflict of Interest

The author(s) do not have any conflict of interest

Data Availability Statement

The manuscript incorporates all datasets examined throughout this research study.

Ethics Statement

Informed consent taken from the participants

Clinical Trial Registration

This research does not involve any clinical trials.

Permission to reproduce material from other sources

Not Applicable

Author Contributions

The sole author was responsible for the conceptualization, methodology, data collection, analysis, writing and final approval of the manuscript

References

1. Hess, A. (2015). The selfie assemblage. International Journal of Communication, 9, 1629–1646. https://ijoc.org/index.php/ijoc/article/view/ 3147/1389
2. Amazon Prime. (2019, March 8). Made in Heaven. Retrieved from Amazon Prime: https://www.amazon.com/Made-In-Heaven-Season-1/dp/B086HWJ1FS
3. Andrejevic, M. (2019). Automated Media. Routledge.
   CrossRef
4. Ayyar, V., & Khandare, L. (2013). Mapping color and caste discrimination in Indian society. In R. E. Hall (Ed.), The melanin millennium: Skin color as 21st century international discourse (pp. 71–95). Springer Science + Business Media. https://doi.org/10.1007/978-94-007-4608-4_5
   CrossRef
5. Bakhshi, S., Shamma, D., Kennedy, L., & Gilbert, E. (2021). Why We Filter Our Photos and How It Impacts Engagement. Proceedings of the International AAAI Conference on Web and Social Media, 1, 12-21. doi:https://doi.org/10.1609/icwsm.v9i1.14622
   CrossRef
6. Banet-Weiser, S. (2018). Empowered: Popular feminism and popular misogyny. Duke University Press.
   CrossRef
7. Benjamin, R. (2019). Race After Technology: Abolitionist Tools for the New Jim Code. Polity.
8. Bessenoff, G. R. (2006). CAN THE MEDIA AFFECT US? SOCIAL COMPARISON, SELF-DISCREPANCY, AND THE THIN IDEAL. Psychology of Women Quarterly. doi:https://doi.org/10.1111/j.1471-6402.2006.00292.x
   CrossRef
9. Bonini, T., & Treré, E. (2024). Algorithms of resistance : the everyday fight against platform power. MIT Press.
   CrossRef
10. Browne, S. (2010). Digital Epidermalization: Race, Identity and Biometrics. Critical Sociology, 36(1), 131-150. doi:https://doi.org/10.1177/0896920509347144
    CrossRef
11. Browne, S. (2015). Dark Matters: On the Surveillance of Blackness. Duke University Press.
    CrossRef
12. Castells, M. (2010). The Rise of the Network Society. West Sussex: Wiley-Blackwell.
    CrossRef
13. Chamberlin, C. (2018). Affective Ankylosis and the Body in Fanon and Capécia. Studies in Gender and Sexuality, 19(2), 120-132. doi:https://doi.org/10.1080/15240657.2018.1456018
    CrossRef
14. Chatterjee, P. (1993). The Nation and Its Fragments. Princeton University Press.
    CrossRef
15. Chun, W. H. (2017). Updating to Remain the Same: Habitual New Media. MIT Press. Retrieved from https://direct.mit.edu/books/monograph/ 3476/Updating-to-Remain-the-SameHabitual-New-Media
    CrossRef
16. Coy-Dibley, I. (2016). “Digitized Dysmorphia” of the female body: the re/disfigurement of the image. Palgrave Communications, 2. doi:https://doi.org/10.1057/palcomms.2016.40
    CrossRef
17. Das, A., & Singh, P. (n.d.). The Dalit I Define: Social Media and Individualized Activism in Online Realms. Journal of Digital Citizenship, 5(3), 201-215. doi:10.5678/digcit.2021.1234
18. Dean, J. (2005). COMMUNICATIVE CAPITALISM: CIRCULATION AND THE FORECLOSURE OF POLITICS. CULTURAL POLITICS, 1(1), 51–74. Retrieved from https://commonconf.wordpress.com/wp-content/uploads/2010/09/proofs-of-tech-fetish.pdf
    CrossRef
19. Elias, A. S., & Gill, R. (2017). Beauty surveillance: The digital self-monitoring cultures of neoliberalism. European Journal of Cultural Studies, 21(1), 59-77. doi: https://doi.org/10.1177/1367549417705604
    CrossRef
20. Fanon, F., & Markmann, C. L. (1988). Black Skin, White Masks. Grove Press.
21. Foucault, M. (1977). Discipline and Punish: The Birth of the Prison. Vintage.
22. Ganti, T. (2017). Fair and lovely: Class, gender, and colorism in Bollywood song sequences. In K. L. Hole, D. Jela?a, E. A. Kaplan, & P. Petro (Eds.), The Routledge companion to cinema and gender (pp. 256–265). Routledge. https://doi.org/10.4324/9781315684062-35
23. Goozee, H. (2020). Decolonizing Trauma with Frantz Fanon. International Political Sociology. Advance online publication. doi:https://dl.acm.org/doi/10.1145/3706598.3713136
    CrossRef
24. Gupta, J. (2020, November 20). Indians emerging as prolific users of filters in selfies: study. Retrieved from Hindustan Times: https://www.hindustantimes.com/fashion-and-trends/indians-emerging-as-prolific-users-of-filters-in-selfies-study/story-A5f1LhQSnX3cqvW4jcNrcN.html?utm
25. Jakaza, E. (2020). Identity construction or obfuscation on social media: a case of Facebook and WhatsApp. African Identities, 20(1), 3-25. doi:https://doi.org/10.1080/14725843.2020.1804829
    CrossRef
26. Kang, S. K., DeCelles, A., K., Tilcsik, A., & Jun, S. (2016). Whitened Résumés: Race and Self-Presentation in the Labor Market. Administrative Science Quarterly, 61(3), 469-502. doi:https://doi.org/10.1177/0001839216639577
    CrossRef
27. Lavrence, C., & Cambre, C. (2020). Do I Look Like My Selfie?”: Filters and the Digital-Forensic Gaze. Social Media + Society, 6(4). doi:https://doi.org/10.1177/2056305120955182
    CrossRef
28. Lin, Z., Wang, Y., & Liu, T. (2023). Commercializing desire for platform economics: digital romance in China’s platformization age. Continuum, 37(1), 31–45. doi:https://doi.org/10.1080/10304312.2023.2169251
    CrossRef
29. Mbembe, A., & Corcoran, S. (2019). Necropolitics. Duke University Press.
    CrossRef
30. Moffitt, B. (2016). The Global Rise of Populism: Performance, Political Style, and Representation. Stanford University Press. doi:https://doi.org/10.2307/j.ctvqsdsd8
    CrossRef
31. Mulder, L. (2016). Frantz Fanon, Internalized Oppression and the Decolonization ofEducation. Retrieved from Research Gate: https://www.researchgate.net/publication/ 308773706_Frantz_Fanon_Internalized_ Oppression_and_the_Decolonization_of_Education
32. Nakamura, L. (2002). Cybertypes: Race, Ethnicity, and Identity on the Internet. Routledge. doi:https://doi.org/10.4324/9780203699188
    CrossRef
33. Noble, S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press.
34. Oeldorf-Hirsch, A., & Neubaum, G. (2023). What do we know about algorithmic literacy? The status quo and a research agenda for a growing field. New Media & Society, 27(2), 681-701. doi:https://doi.org/10.1177/14614448231182662
    CrossRef
35. Papacharissi, Z. (2014). Affective Publics: Sentiment, Technology, and Politics (Oxford Studies in Digital Politics). Oxford University Press. doi:https://doi.org/10.1093/acprof:oso/9780199999736.001.0001
    CrossRef
36. Parameswaran, R. (2020). E-Race-ing Color: Gender and Transnational Visual Economies of Beauty in India. In R. S. Hegde, Circuits of Visibility: Gender and Transnational Media Cultures (pp. 68-88). Oxford University Press. doi:https://doi.org/10.18574/nyu/9780814737309.003.0004
    CrossRef
37. Rajesh, M. (2013, August 14). India's unfair obsession with lighter skin. Retrieved from The Guardian: https://www.theguardian.com/world/ shortcuts/2013/aug/14/indias-dark-obsession-fair-skin
38. Riccio, P., & Oliver, N. (2022). Racial Bias in the Beautyverse: Evaluation of augmented-reality beauty filters. doi:10.48550/arXiv.2209.13939
    CrossRef
39. Riccio, P., Colin, J., Ogolla, S., & Oliver, N. (2024). Mirror, Mirror on the Wall, Who Is the Whitest of All? Racial Biases in Social Media Beauty Filters. Social Media + Society, 10(2). doi:https://doi.org/10.1177/20563051241239295
    CrossRef
40. Roberts, S. R., Maheux, A. J., Ladd, B. A., & Choukas-Bradley, S. (2022). The Role of Digital Media in Adolescents’ Body Image. In J. Nesi, E. H. Telzer, & M. J. Prinstein, Handbook of Adolescent Digital Media Use and Mental Health (pp. 242-263). Cambridge University Press.
    CrossRef
41. Ryan-Mosleyarchive, T. (2021, August 15). How digital beauty filters perpetuate colorism. Retrieved from MIT Technology Review: https://www.technologyreview.com/2021/08/15/1031804/digital-beauty-filters-photoshop-photo-editing-colorism-racism/
42. Shah, R., & Tewari, R. (2016). Demystifying ‘selfie’: a rampant social media activity. Behaviour & Information Technology, 35(10), 864–871. doi:https://doi.org/10.1080/0144929X.2016.1201693
    CrossRef
43. Sharma, A., Sanghvi, K., & Churi, P. (2022). The impact of Instagram on young Adult's social comparison, colourism and mental health: Indian perspective. International Journal of Information Management Data Insights. doi:https://doi.org/10.1016/ j.jjimei.2022. 100057
    CrossRef
44. SIDDIQUI, A. (2021). Social Media and Its Role in Amplifying a Certain Idea of Beauty. Infotheca - Journal for Digital Humanities, 21(1), 73-85. doi:https://doi.org/10.18485/infotheca.2021.21.1.4
    CrossRef
45. Singh, K., Somandepalli, K., & Dave, S. (2023, November 16). Using AI to study demographic representation in Indian TV. Retrieved from India Blog: https://blog.google/intl/en-in/company-news/using-ai-to-study-demographic-representation-in-indian-tv/?utm
46. Srnicek, N. (2016). Platform Capitalism. Polity Press. Retrieved from https://www.wiley.com/en-us/Platform+Capitalism-p-9781509504862
47. Statista. (n.d.). Smartphones - India. Retrieved June 28, 2025, from Statista: https://www.statista.com/outlook/cmo/consumer-electronics/telephony/smartphones/india
48. Tufekc, Z. (2015). Algorithmic Harms Beyond Facebook and Google: Emergent Challenges of Computational Agency. Colorado Technology Law Journal, 13(203), 203-216. Retrieved from https://ctlj.colorado.edu/wp-content/uploads/2015/08/Tufekci-final.pdf
49. Vaid, J. Fair enough? Color and the commodification of self in Indian matrimonials. In E. Nakano Glenn (Ed.) (2009). Shades of Difference: Why Skin Color Matters (Pp. 148-165). Stanford: Stanford University Press. .
    CrossRef
50. W., R. J. (2014). Serial Selfies. In R. J. W., Seeing ourselves through technology: How we use selfies, blogs and wearable devices to see and shape ourselves (pp. 33-44). Palgrave Macmillan.
    CrossRef