The findings reveal an “Authenticity Paradox”: the lack of traditional human touch in AIGC does not alienate Generation Z; rather, the resulting “mindful friction”—quantified by a significant negative path effect between sentiment and identity—functions as a subcultural filter.
Abstract
Generative AI (AIGC) is fundamentally reshaping the landscape of digital content, yet its specific predictive relationship with the cultural identity of “digital natives” (Generation Z) remains underexplored compared to traditional User-Generated Content (UGC). While AIGC offers efficiency and aesthetic novelty, it lacks the inherent “human touch” of UGC. This study investigates this divergence by analyzing 11,628 comments from the Douyin platform. We employed a mixed-method approach combining semantic network analysis and structural equation modeling (SEM) to compare user responses across emotional, technological, and social dimensions. Our findings reveal a distinct “two-path” mechanism: while UGC fosters identity through a “Warm Path” of emotional resonance, AIGC is structurally associated with a significantly more robust identity profile via a “Cool Path” predicted by technical curiosity and exclusive topic circling. Crucially, we identify an “Authenticity Paradox”: the lack of traditional human touch in AIGC does not alienate Generation Z; rather, the resulting “mindful friction”—quantified by a significant negative path effect between sentiment and identity (β=−0.370) —functions as a subcultural filter.
It is argued that AI-driven “style replication” is fundamentally a negotiation of social relations, one that recentres platform power and reorganises the conditions of human-AI collaboration.
Meng Liang, Fangzhou Zhang· Media, Culture & Society· 1 citation
This article examines the entanglement of artificial intelligence (AI) and popular digital culture through a case study of the music video “Preman Anggaran” by the band Parikesit. The video features an AI generated female figure, modestly veiled yet styled in casual, tomboyish attire, performing socially charged rap-punk lyrics that critique political corruption and class inequality. Employing Norman Fairclough’s model of Critical Discourse Analysis (CDA) in conjunction with theoretical insights from Cultural Studies, this study interrogates the ideological, representational, and resistant dimensions embedded in both the textual and visual content. The analysis reveals how confrontational lexical choices, oppositional semantics, and direct narrative structures function as linguistic strategies of symbolic resistance. The production process, which relies on AI generated aesthetics, subverts conventional norms of visual representation and enables alternative forms of cultural expression. The video’s dissemination via digital platforms such as YouTube and TikTok exemplifies a non-institutional mode of circulation, while its reception demonstrates participatory engagement and affective resonance among politically conscious audiences. Ultimately, this study foregrounds the methodological relevance of CDA and cultural studies in navigating the cultural politics of algorithmically mediated expression, highlighting how AI can be mobilized as a vehicle for symbolic intervention and alternative articulation in contemporary popular culture.
Eka Dian Savitri· JURNAL SOSIAL HUMANIORA· 0 citations
The digital platforms that are algorithmically curated shape young users encounter with culture, aesthetics and consumption. This study examines the brainrot culture that is absurd, repetitive and short term which contributes to identity formation among Generation Z audience. The particular focus is on viral collectibles which become strong material expressions of digitally mediated selfhood and societal acceptance. The research addresses a gap in digital and cultural studies by linking algorithmic feeds, engagement patterns and symbolic practices that become a very crucial part of the Generation Z personality and behavior. Using an exploratory quantitative cross-sectional research design, primary data was collected from the Generation Z respondents through structured interviews. The descriptive statistical techniques were employed to understand the relationships between algorithmic personalization, brainrot content exposure, psychological engagement, behavioral outcomes and collectible specific identity signaling. The findings of the study reflect deep daily engagement of Generation Z with algorithmic driven social interaction platforms and unaccountable exposure to brainrot aesthetic. The brainrot culture should be understood not as a trivial content but as an adaptive mode within saturated attention economies. The study aims to examine objectives related to how algorithmically dominated brainrot content and viral collectibles curate the identity formation and symbolic consumption among the Generation Z audience. This research contributes to global debates on digital platform governance, youth micro-cultures and digitally mediated identity formation in terms of values, behaviors and symbolic consumption.
Manya Kalra, Anandita Goenka· Discover Global Society· 0 citations
This qualitative interpretive case study investigates how AI guidance influences adolescents’ identity formation in relation to Ubuntu values and proposes the Ubuntu-AI Harmonization Model (UAHM), a framework designed to guide the ethical integration of AI in education while upholding indigenous value systems.
Revi Zhakata, William Muchono, Jigu Katsande et al.· Ngenani: The Zimbabwe Ezekie...· 0 citations
This poster presents an empirical study examining how creative intent evolves during interaction with Dreamina, a text-to-image generative model. Moving beyond the assumption that creative intent is fixed prior to action, we propose an entangled mediation framework—drawing on post-phenomenological mediation theory and Frauenberger’s Entangled HCI—to analyze the recursive interplay between human decision-making and model outputs. Through a mixed-methods study with 10 experienced creators using think-aloud protocols, process logging, and retrospective interviews, we identify three interaction patterns: local correction, gradual emergence, and intent reorganization. Our findings indicate that creative intent functions as a processual attribute shaped through iterative generation–evaluation–modification cycles, rather than a stable precondition. We further observe that creators’ perceptions of authorship are tied to their locus of decision-making rather than the proportion of AI-generated content. These findings contribute to understanding human–AI co-creativity as a situated, relational process and offer preliminary implications for designing generative tools that support flexible interaction strategies.
