Zoomwork

Mapping Gen Z’s Work Imaginaries through TikTok and Instagram Visual Cultures

Team Members

Summer School facilitators
Giulia Giorgi, University of Milan
Luca Giuffrè, University of Milan

Researchers (Alphabetical Order)
Jed Senthil, Nanyang Technological University, Singapore (NTU)
Natália Dias, Federal University of Minas Gerais (UFMG)
Özlem Ozan, Yaşar University
Polina Indzhebai, Tilburg University
Wei Wei, Justus Liebig University Giessen (JLU)
Ying Luo, Norwegian University of Science and Technology (NTNU)

ZOOMWORK project contributors
Camilla Volpe, University of Milan
Sabine Niederer, Amsterdam University of Applied Sciences
Carlo de Gaetano, Amsterdam University of Applied Sciences

Designer facilitator
Asia Capezzuoli

Contents

Summary of Key Findings

ZOOMWORK investigated how imaginaries of work associated with Gen Z circulate across TikTok and Instagram in seven country-language contexts: Brazil, China, Italy, Norway, Russia, Singapore and Türkiye. The initial collections comprised 6,760 platform records: 2,941 from TikTok and 3,819 from Instagram. The analysis combined multilingual query design, hashtag and keyword networks, image exploration and collaborative qualitative interpretation.

The findings identify a clear differentiation between the two platforms. Instagram generally organised work through professional, institutional and career-oriented vocabularies. Its content frequently addressed recruitment, employability, leadership, human resources, professional development and entrepreneurship. Gen Z was often presented as an employee category to be understood, trained, managed or integrated into organisational strategies. TikTok foregrounded everyday workplace experiences, humour, memes, generational comparisons, corporate satire and algorithmic visibility. On this platform, Gen Z more frequently appeared as an active narrator commenting on work, mocking workplace conventions and negotiating the boundaries between employment and personal life.

Across the seven contexts, three major work imaginaries recurred. The first concerned AI, employability and the future of work. AI was usually framed as a workplace capability connected to productivity, creativity, career adaptation and digital skills, although concerns about automation, surveillance, deskilling and job displacement were also present. The second concerned platformed corporate identities. Trends such as the “Corporate Baddie” aesthetic turned professional identity into a lifestyle performance expressed through vlogs, sketches, clothing, routines and office humour. The third concerned exploitation, wellbeing and mental health. Burnout, labour rights, precarity, fair pay, psychological sustainability and work–life balance appeared across several contexts.

These shared themes acquired distinct local meanings. Brazil foregrounded labour rights, the work–study burden and debates over exhausting work schedules. China highlighted livestreaming, platform entrepreneurship and visibility-based digital labour. Italy concentrated on precarity, exploitation, mental wellbeing and work–life balance. Norway connected work to vocational pride, blue-collar occupations, flexible employment and outdoor-oriented lifestyles. Russia used humour and generational comparison to discuss workplace behaviour and employer stereotypes. Singapore framed work through four interconnected imaginaries: career optimisation and employability, personal development and professional success, flexible and entrepreneurial careers, and AI-augmented future employability. Across the Singaporean corpus, work was consistently represented as a pathway towards continuous self-improvement rather than merely employment. Instagram emphasised career opportunities, recruitment and professional development, while TikTok foregrounded AI adaptation, workplace humour and corporate identity. Türkiye combined entrepreneurial aspiration, technological opportunity, economic uncertainty and contested ideas about legitimate work.

A significant finding is that Gen Z’s platform discourse cannot be reduced to a general rejection of work. Across the datasets, young people questioned exploitative working cultures, unconditional loyalty and the expectation that professional life should take precedence over wellbeing. They continued to value employment, achievement and professional development, but sought a different social contract based on reciprocity, meaningful activity, fair compensation, flexibility and sustainable boundaries.

1. Introduction

The Summer School project was developed as part of the broader ZOOMWORK research project (2025–2027), funded by Fondazione Cariplo, which investigates how social media platforms contribute to the construction and circulation of Gen Z’s imaginaries of work. The project is situated at the intersection of youth studies, sociology of work, platform studies, digital methods and visual methodologies. Within the Summer School, we focused on one specific component of this wider research agenda: the visual and digital methods analysis of work-related content on TikTok and Instagram.

Work is increasingly imagined before it is experienced. For many young adults, TikTok and Instagram now function as primary sites through which expectations about careers, office culture, AI, entrepreneurship, burnout and employability are encountered long before entering full-time employment. Social media therefore does not merely reflect labour markets—it actively shapes cultural understandings of what work is, what good work looks like, and what kinds of futures appear desirable or attainable.

Existing studies have examined Gen Z's workplace values, AI adoption, platform labour and digital entrepreneurship. However, much of this work focuses on surveys, organisational behaviour or individual attitudes. Far less attention has been given to how social media collectively constructs work as a cultural imaginary across multiple countries and platforms.

The project approached Gen Z’s culture of work not as a fixed set of generational preferences, but as a historically and materially situated configuration produced within unstable labour markets, platformised environments, meritocratic promises and shared media repertoires. Drawing on debates on neoliberalism and the entrepreneurial self, young people were understood as subjects increasingly asked to invest in employability, adaptability, visibility and self-improvement, while navigating structural constraints that remain largely outside their control (Harvey, 2007; Colombo, Rebughini and Domaneschi, 2022).

This perspective was especially relevant for analysing platform-based narratives of work. Concepts such as hope labour and aspirational labour helped us frame the ways in which visibility, networking, self-presentation and unpaid or underpaid experience are often imagined as investments in future opportunities (Kuehn and Corrigan, 2013; Duffy, 2017; Mackenzie and McKinlay, 2021). At the same time, the project treated Gen Z as a media generation socialised within commercial, algorithmic and highly visual platform environments, rather than as a naturally “digital” or homogeneous cohort (Bolin, 2016; Jones and Shao, 2011).

TikTok and Instagram were therefore significant research sites because they provide formats, aesthetics, memes, hashtags, captions and platform vernaculars through which work, identity, success, burnout, refusal and self-realisation are made meaningful. Following the lens of sociotechnical imaginaries, the project examined how ideas of autonomy, flexibility, visibility, efficiency and individual responsibility circulate within environments organised by algorithms, metrics, recommendation systems and monetisation structures (Jasanoff and Kim, 2013). In this sense, the Summer School explored how young people imagine, negotiate and contest work within platforms that both enable bottom-up meaning-making and organise visibility through infrastructural and commercial logics.

Building on this framework, the Summer School developed a cross-platform and cross-cultural comparison covering Brazil, China, Italy, Norway, Russia, Singapore and Türkiye. Working with researchers familiar with the languages and cultural contexts under investigation allowed the project to move beyond an exclusively English-language understanding of Gen Z and work. The comparison examined how globally circulating themes (e.g. AI, corporate identity, burnout, entrepreneurship and work–life balance) were translated into local vocabularies, controversies and visual styles. It also considered how Instagram and TikTok assigned different levels of visibility to professional advice, institutional communication, humour, personal experience and workplace critique.

2. Initial Data Sets

The project did not begin with a pre-existing dataset. The TikTok and Instagram collections were created during the Summer School through a shared research protocol applied to seven country-language contexts: Brazil, China, Italy, Norway, Russia, Singapore and Turkey.

3. Research Questions

The project addressed the following main research question:

What sociocultural imaginaries around “work” and “Gen Z” emerge in social media discourse across different language and cultural contexts?

This question was developed through four supporting research questions:

1. How do TikTok and Instagram differ in their visual and discursive construction of Gen Z’s relationship with work?

2. Which recurring aesthetics, memes, formats, hashtags and platform vernaculars are used to represent work, professional identity and workplace experience?

3. How are global themes such as AI, burnout, corporate culture, precarity, entrepreneurship and work–life balance interpreted and negotiated across different national contexts?

4. How can visual and AI-assisted digital methods support the mapping and critical interpretation of Gen Z’s work imaginaries?

The project began with three main expectations. First, TikTok was expected to foreground performative, affective and memetic narratives, while Instagram was expected to contain more curated, professional and institutionally oriented content. Second, work-related discourse was expected to combine aspiration with exhaustion, presenting flexibility and self-realisation alongside precarity, burnout and frustration. Third, AI was expected to operate simultaneously as an empirical theme within work-related content and as an experimental methodological resource for visual analysis.

The comparative design also left room for unexpected findings. In particular, the project examined whether AI would be equally central across all contexts, whether “anti-work” content represented an actual rejection of employment, and whether content about Gen Z was predominantly produced by young workers themselves or by employers, consultants, human resources professionals and career-oriented accounts.

4. Methodology

4.1 Research Design

The project adopted a comparative, multilingual and cross-platform digital methods design. Each researcher was responsible for a specific country-language context and contributed linguistic knowledge, cultural interpretation and familiarity with locally relevant workplace debates. The comparison covered Brazil, China, Italy, Norway, Russia, Singapore and Turkey.

The research followed the principle of “following the medium” by adapting the collection and analytical procedures to the technical and cultural characteristics of TikTok and Instagram. The platforms were treated as distinct research environments rather than interchangeable sources of social media content. Their search interfaces, recommendation systems, content formats and vernacular practices influenced both the material retrieved and the types of work discourse visible within each dataset.

4.2 Research Preparation

Before starting the collection, the team created new research accounts on TikTok and Instagram, where platform access permitted. The accounts were assigned an age of 25 to establish a broadly comparable young-adult user profile across the different cases. Searches were conducted through the platforms’ native interfaces.

In selected cases, researchers also used VPN connections to explore whether geographical location affected the content returned by the platforms. These measures could not remove platform personalisation or guarantee identical search conditions, but they made the collection process more systematic and allowed the team to reflect on the influence of location, account configuration and recommendation systems.

The Chinese case required a context-specific adaptation because of differences in access to Instagram, TikTok and Douyin. These technical and infrastructural conditions influenced the type of Chinese-language material available, particularly content concerning livestreaming, cross-border commerce, platform entrepreneurship and AI-supported creation.

4.3 Definition of Entry Points

The collection began with the general keyword and hashtag “Gen Z work”, translated into Italian, Norwegian, Russian, Brazilian Portuguese, Turkish and Chinese, while English was used for the Singaporean case.

Following the principles of “following the medium” and “following the user” (Caliandro and Gandini, 2016), each team member subsequently expanded the initial entry point through variations and permutations of the original query. Researchers adapted their searches to the terminology encountered on each platform, the language used by relevant creators and accounts, and the platform-specific formats and vernaculars that became visible during the exploration.

The resulting entry points combined references to Gen Z with terms related to work, employment, office life, careers and working life. Depending on the local context, the searches were further enriched with expressions related to AI, platform labour, generational comparison, livestreaming, work–study experiences, labour rights, precarity and work–life balance.

Table 1. List of entry points per country and total number of posts collected.

4.4 Data Collection and Corpus Construction

Relevant TikTok and Instagram publications were captured using Zeeschuimer and subsequently processed through 4CAT. Depending on platform availability, the collected material included captions, hashtags, publication URLs, platform metadata, thumbnails, images and still frames extracted from videos. The visual materials produced through 4CAT allowed the researchers to move between corpus-level patterns and the qualitative examination of individual posts.

The resulting collection comprised 6,168 records, including 2,941 TikTok records and 3,227 Instagram records. The number of records differed across cases because of variations in platform access, search functionality, language, query breadth, account personalisation and the amount of relevant content returned by each search.

The collections were cleaned and standardised before analysis. Country and platform identifiers were added, hashtags were extracted and harmonised, and duplicate or irrelevant records were removed where identified. Original-language keywords and hashtags were preserved alongside their English translations. Retaining both versions was essential because direct translation can obscure wordplay, generational labels, platform slang and culturally specific meanings.

Where available and ethically appropriate, engagement indicators and limited account-level information were retained to contextualise the publications. The analysis nevertheless concentrated on content, hashtags, visual formats and platform vernaculars, rather than on profiling individual users.

The record counts should be interpreted as descriptions of the collected corpora, not as measures of the relative importance of Gen Z work discourse in each country. The datasets are exploratory and do not provide representative samples of national populations or of Gen Z users as a whole. They document the results made visible by platform search systems during a specific period, through specific queries and research accounts. Their analytical value lies in the comparison of recurring narratives, formats, visual styles and thematic associations across platforms and language contexts.

4.5 Network and Hashtag Analysis

The first analytical strand examined hashtags and their relationships in their original languages. A cross-platform bipartite network was produced to connect the seven country-language contexts with the hashtags found in their datasets. The network was explored in Gephi to identify language-specific clusters, shared cross-country hashtags and bridging terms.

The network made it possible to distinguish locally concentrated vocabularies from globally circulating platform terms. Hashtags such as #genz, #work, #office, #corporate and #ai connected several contexts, while local equivalents (including #зумеры, #zkuşağı, #generasjonz, #geraçãoz and Chinese-language generational labels) formed more culturally specific clusters. Platform visibility tags such as #fyp, #viral and #foryou also largely created connections across languages.

Additional co-hashtag explorations concentrated on three themes identified during the collaborative analysis: AI and the future of work; corporate identities and “Corporate Baddie” content; and exploitation, mental health and work–life balance. These networks were used as exploratory maps rather than as self-sufficient explanations. Their interpretation was checked against captions, visual materials and locally informed readings of the content.

4.6 Visual Exploration and Qualitative Interpretation

Researchers independently prepared interpretive memos before jointly comparing findings across cases. Through iterative discussion, themes were refined until conceptual agreement was reached.

Images and video thumbnails were explored with ImaGi, an image exploration tool developed for the project. This stage supported the identification of recurring visual formats, including office sketches, talking-head videos, workplace memes, corporate fashion, routine vlogs, screenshots, motivational graphics, blue-collar occupational imagery and AI-generated visuals.

Visual similarity was used as an entry point for qualitative interpretation. Researchers inspected clusters and individual images to determine whether visual proximity corresponded to shared topics, styles or platform conventions. This step was necessary because visually similar images can communicate different meanings, while visually heterogeneous posts can participate in the same discourse.

The collaborative analysis combined visual and qualitative exploration with close reading of captions, hashtags and locally relevant platform trends. Each researcher prepared an interpretive memo describing general patterns, country-specific findings, the role of AI and differences between TikTok and Instagram. These memos were then compared to establish shared themes and points of divergence.

4.7 Ethical and Methodological Limitations

Given the exploratory, cross-platform and multilingual nature of the project, the findings need to be read in relation to the specific conditions under which the data were collected and interpreted.

4.8 Research Ethics and Limitations

The analysis was conducted primarily at the level of aggregated patterns, hashtags and visual formats. Its purpose was to interpret platform discourse rather than evaluate or profile individual creators. Personally identifying information was excluded from the analytical outputs whenever it was unnecessary for addressing the research questions.

The datasets are exploratory and do not constitute representative samples of Gen Z users, national populations or platform content. They reflect the results returned by TikTok and Instagram during a specific period, through selected queries, research accounts and locations. The findings should therefore be understood as an account of the work imaginaries made visible by the platforms under these particular research conditions, rather than as representative measurements of Gen Z attitudes in the seven countries.

Platform search systems are dynamic, personalised and partly opaque. Search results may vary according to time, geographical location, language settings, account history, previous interactions and algorithmic recommendation. The creation of comparable research accounts and the use of VPNs in selected cases made some of these conditions more explicit, but could not eliminate personalisation or guarantee fully equivalent collection conditions across platforms and countries.

The seven datasets were developed from a shared initial entry point and subsequently expanded through locally relevant variations and permutations. This strategy allowed the researchers to follow platform vernaculars and culturally specific expressions, but it also introduced differences in query breadth, thematic focus, relevance and corpus size. Direct quantitative comparisons between countries should consequently be approached with caution. Differences in record counts may reflect platform accessibility, search functionality and query design as much as the relative visibility of a topic in a given context.

Cross-language comparison presented a further challenge. Original keywords and hashtags were preserved alongside English translations, and local researchers contributed linguistic and cultural knowledge to the analysis. Even so, jokes, generational labels, wordplay and platform-specific expressions cannot always be transferred fully between languages. Translation may therefore reduce semantic nuance or obscure locally meaningful references.

The analysis relied substantially on hashtags, captions, thumbnails and selected video stills. These materials supported the identification of recurring themes and visual formats, but could not capture every element of the publications. Sounds, editing rhythms, gestures, comments and complete video narratives were examined selectively rather than systematically. Hashtags are also imperfect indicators of meaning: they may serve promotional or algorithmic purposes and do not provide complete information about a post, its creator or its intended audience.

It was not always possible to determine the age or generational identity of content creators. A considerable amount of material concerning Gen Z was produced by employers, recruiters, consultants, institutional accounts and users from other generations. The datasets therefore include both self-representations produced by young people and external representations of Gen Z as an employee category. This distinction was considered during the qualitative interpretation but could not be established consistently for every record.

The cross-platform comparison was also affected by differences in platform architecture and data availability. TikTok and Instagram provide different search functions, metadata and modes of access, limiting strict equivalence between the two collections. The Chinese case required particular contextual caution because Instagram and TikTok occupy a different infrastructural position in mainland China and cannot be treated as direct equivalents of Douyin.

These limitations define the scope of the comparison without diminishing its exploratory value. The study provides a situated account of how work-related imaginaries became visible through selected platforms, languages, queries and research conditions. It also establishes a basis for future longitudinal, multimodal and comparative research incorporating complete videos, comments, sounds, creator networks and more systematic analysis of platform personalisation.

5. Findings

The findings reveal a combination of recurring cross-platform and cross-cultural themes and more context-specific narratives. While AI, corporate identity, burnout, exploitation and work–life balance appear across several datasets, their meanings, visual forms and emotional registers vary according to platform cultures and national contexts.

5.1 AI, Employability and the Future of Work

AI appeared most strongly in the Singaporean, Turkish and Chinese datasets. Across these cases, it was frequently presented as an expected workplace capability rather than as a distant technological development. Content encouraged users to learn AI tools, improve productivity, prepare for AI-supported interviews, automate tasks and adapt their skills to changing labour markets.

Singapore contained the clearest connection between AI and employability. AI was associated with career guidance, productivity, job applications, interview preparation, technology careers and future readiness. It was usually framed as augmentation: workers were expected to combine AI tools with creativity, communication, judgement and problem-solving.

Network analysis further showed that AI was embedded within a broader employability discourse rather than existing as a standalone technological topic. In the Singapore dataset, hashtags such as #AI, #ArtificialIntelligence, #ChatGPT, #Automation, #FutureOfWork, and #AItools clustered with career-oriented hashtags relating to productivity, internships, interviews and professional development. Rather than portraying AI as replacing workers, creators positioned AI literacy as an increasingly expected workplace competency.

Visual analysis reinforced this interpretation. Posts frequently showcased AI being applied in architecture, education, office work and recruitment, presenting generative AI as a practical workplace capability. Unlike widespread media narratives emphasising "AI replacing jobs," Singaporean creators more often framed AI as augmenting human judgement, creativity and problem-solving while encouraging continuous learning and technological adaptation.

At the same time, the discourse expressed anxiety about job displacement and the need to remain continuously adaptable.

In China, AI was linked to content creation, advertisements, visual effects, short dramas and platform entrepreneurship. Official or semi-official campaigns encouraging AI-generated creation contributed to an imaginary in which technology expanded the possibilities of digital labour. These opportunities remained connected to platform visibility, monetisation systems and algorithmic competition.

In Türkiye, AI appeared within a broader field of software, entrepreneurship, digital transformation and self-investment. It promised productivity and new career possibilities but also raised concerns about automation, surveillance, deskilling and economic uncertainty.

AI was far less prominent in Norway and appeared mainly as a secondary topic in several other cases. Similarly, AI was largely absent in Russia, and if mentioned was largely discussed in terms of work and tasks optimisation. This uneven distribution is important. It shows that AI has not become the universal centre of Gen Z work discourse. Its relevance depended on local labour-market narratives, query design and the dominant uses of each platform.

A further counter-intuitive result came from Singapore. Although one of the searches explicitly included “AI slop,” the dataset contained almost no sustained discussion of this concept. AI-related content concentrated on practical skills, employability and productivity instead of low-quality synthetic content.

5.2 Corporate Identity and the “Corporate Baddie”

The second cross-context imaginary concerned the transformation of professional identity into a visible lifestyle. The “Corporate Baddie” trend provided a particularly clear example. Corporate work was represented through fashion, office routines, coffee, commuting, desk arrangements, professional confidence and the performance of competence. One TikTok creator films a "day in my life as a corporate girl," beginning with coffee preparation, outfit selection, office commuting and aesthetic desk setup. Although presented humorously, the video frames professional identity as a lifestyle performance rather than simply employment.

These posts presented work as part of personal identity and self-presentation. Vlogs and “day in my life” formats connected employment with consumption, appearance, ambition and everyday routines. The professional self became a platformed persona whose value depended partly on being visually recognisable and culturally engaging.

This imaginary had different local tones. In some Norwegian and Brazilian content, corporate and professional lifestyles were presented aspirationally. Russian content more frequently used humour, irony and generational comparison to expose the absurdities of office life and criticize work culture in general. Italian content moved between corporate humour and criticism of precarious or exploitative employment.

The corporate imaginary was also gendered. Many office-aesthetic and “Corporate Baddie” posts used femininity, clothing and beauty as resources for narrating professional identity. This made the workplace visually attractive and culturally legible, while leaving open questions about the unpaid aesthetic and emotional labour required to maintain a platformed professional persona.

5.3 Surviving work: Burnout, Mental Health, and Exploitation

Burnout, mental health, work–life balance, fair pay and labour rights formed the third recurring imaginary. Across several contexts, Gen Z was described as less willing to accept overtime, managerial disrespect, unstable conditions or the expectation that work should dominate personal life.

This discourse did not amount to a rejection of work. It represented an attempt to redefine legitimate employment. Work was expected to provide economic security, meaning and opportunities for development, but it was also expected to respect psychological wellbeing, personal time and reciprocal obligations.

Italy strongly foregrounded work–life balance, precarity, exploitation and mental wellbeing. TikTok combined office humour with critical hashtags related to labour conditions, while Instagram contained more professional, human-resources and organisational vocabularies.

Brazil connected wellbeing to labour rights and the material organisation of working time. The burden of combining employment and education was especially visible in routine videos and personal narratives. Debates around the six-day working schedule also situated individual exhaustion within a wider political discussion of labour regulation and collective rights.

Russian content frequently expressed anti-overwork positions through humour. Gen Z creators contrasted their willingness to resign, defend personal boundaries or refuse disrespect with older expectations of long-term company loyalty, stress endurance and working overtime. Employers and managers sometimes reproduced stereotypes of Gen Z workers as lazy, emotionally demanding or insufficiently professional.

In Türkiye, precarity and economic uncertainty coexisted with entrepreneurial and self-development discourse. The country-specific analysis also identified contrasting ideas of legitimate work. Some posts defended Gen Z against accusations of laziness by referring to unfair conditions and limited opportunities. Other content, particularly involving blue-collar masculine identities, promoted endurance and physical labour through messages equivalent to “we do not cry, we work.”

5.4 Platform-Specific Ways of Representing Work

The analysis identified a broad division of discursive functions between TikTok and Instagram. Instagram generally framed work through institutional, professional and instrumental vocabularies. Recruitment, job vacancies, internships, leadership, human resources, professional development, networking and entrepreneurship were particularly visible. The platform frequently presented work as a field of opportunities and strategic decisions. Success was associated with obtaining employment, building a career, developing skills and maintaining a recognisable professional identity.

A substantial portion of Instagram content was produced by employers, recruitment agencies, consultants, career coaches and organisational accounts. In these posts, Gen Z often appeared as an object of professional knowledge: a cohort whose expectations, values and behaviour needed to be explained, managed or integrated into the workplace.

TikTok offered a more experiential and culturally expressive representation of work. Office humour, generational satire, point-of-view videos, sketches, routine vlogs, trending sounds and personal storytelling were central. Work was narrated as an everyday experience involving colleagues, managers, emotional boundaries, frustration, boredom and generational misunderstanding.

For example, the Singapore dataset illustrates this platform distinction particularly clearly. Instagram functioned primarily as a career optimisation platform, where recruitment agencies, universities, employers and career influencers circulated advice about internships, employability, motivation and professional development. By contrast, TikTok functioned as a cultural negotiation space, where creators discussed corporate life through humour, AI adaptation, workplace satire and everyday office experiences. The two platforms therefore constructed complementary rather than competing meanings of work.

TikTok also displayed a stronger orientation toward algorithmic visibility. Tags such as #fyp, #foryou, #viral and their local equivalents were integrated into work-related discourse. The desire to enter recommendation feeds was therefore part of how workplace stories were produced and circulated. Platform visibility was especially prominent in the Turkish and Russian dataset but appeared across several contexts.

The contrast does not imply that Instagram contained no humorous material or that TikTok lacked professional advice. It indicates a difference in emphasis. Instagram more often addressed work as a career and organisational issue, while TikTok more often treated it as an experience to be performed, narrated and collectively interpreted.

5.5 Country-Specific Work Imaginaries

5.5.1 Brazil: Work as Social Justice and Life Balance

Brazilian content connected work with generational identity, labour rights, mental health and the practical difficulties of entering the labour market. TikTok foregrounded humour, routine vlogs and the experience of combining work with study. Instagram contained more material on burnout, internships, apprenticeships, entrepreneurship, unions and employment regulation. Work was presented as both a source of individual survival and a collective question of rights and social justice. Brazil illustrates a rights-based work imaginary centred on labour justice.