Research report: How algorithms shape the global visibility of Korean content

The global success of Korean pop culture has long been explained through soft power: state strategy, emotional storytelling, and devoted fandoms carrying K-dramas and K-pop across borders. A new open-access study argues that this explanation is now incomplete. In a paper published in the journal Communication and Change, communication researcher Donghee Shin proposes that the Korean Wave, or Hallyu, has entered a phase he calls “Algorithmic Hallyu,” in which recommendation systems, predictive analytics, and generative AI do not merely distribute Korean content but actively shape what counts as Korean culture and how it is perceived worldwide.

The study, published on 2 September 2026, draws on a qualitative analysis of publicly available materials collected between 2024 and 2025, including transcripts from twelve major public talks and panel discussions involving cultural producers, trade journalism from outlets such as Variety and The Hollywood Reporter, and systematically sampled comment threads and fan forum posts from global platforms like Reddit and YouTube where algorithmic recommendations were explicitly discussed. The curated corpus comprised 45 core documents and more than 200 pages of platform-mediated discourse. All materials were freely accessible and intended for public consumption at the time of analysis.

Importantly, the study is interpretive rather than empirical in the survey sense. The author explicitly states that the research does not aim to measure audience attitudes or produce generalizable behavioral claims. Instead, it seeks conceptual clarification by identifying recurring narratives, interpretive frames, and symbolic patterns in how algorithmic mediation of Korean popular culture is articulated in public discourse. Three complementary analytic procedures were employed: qualitative discourse analysis, AI-assisted simulation, and computational semantic mapping, with the latter two functioning as interpretive supports rather than sources of independent empirical claims.

The AI-assisted simulation component used large language models, specifically GPT-4o and Claude 3.5 at a temperature setting of 0.7, to generate simulated, interview-like responses grounded in the discourse corpus. The models were configured as specialized digital media analysts and prompted with theoretically informed questions about authenticity, emotional engagement, algorithmic visibility, and cultural identity. The author is careful to note that these simulated responses are not treated as empirical accounts of individual experience and are not substitutes for human subjects. All quoted excerpts presented in the findings are identified as AI-generated simulation outputs rather than human participant responses, and human interpretation remained central throughout, with researcher judgment prioritized wherever AI outputs diverged from the interpretively developed themes.

The computational layer used BERTopic with transformer-based embeddings from Sentence-BERT to group semantically related terms, word clusters generated with the Python WordCloud library, and a semantic network built with spaCy, NetworkX, and Gephi. The final topic model yielded a mean coherence score of 0.675 across six topics using the c_v metric, which the author describes as indicating acceptable semantic consistency. The topic clusters showed substantial convergence with the interpretive themes developed through close reading, particularly those related to algorithmic visibility, synthetic performance, negotiated authenticity, and cultural specificity.

The analysis identifies four interrelated themes. The first concerns algorithmic visibility and platformed legibility: how platform infrastructure decisions such as genre tagging, algorithmic categorization, and engagement optimization condition what Hallyu content becomes visible to global audiences, independently of producer intent or audience choice. The discourse materials suggested that platforms like Netflix, TikTok, and YouTube present Korean content less as a national product and more as genre-tagged, emotionally coded commodities. Squid Game, for example, is categorized under globalized genres such as “dark survival thriller,” foregrounding affective intensity over national identity, while Korean dramas are algorithmically reclassified into affective genres like “bittersweet” or “revenge thriller,” a dynamic the paper links to prior scholarship on platform sovereignty.

The second theme examines synthetic performance and algorithmic affect. The study discusses generative idols such as MAVE: and Eternity, created using deep learning and 3D modeling, and HYBE’s experiment with Midnatt, a multilingual AI-driven artist whose voice was synthesized into six languages. Across the simulation outputs, ambivalence toward these AI-generated performers emerged consistently: their technical polish and visual consistency were admired, but many outputs highlighted a distinct lack of emotional resonance, spontaneity, and vulnerability. The author interprets this affective gap as an “uncanny valley” of symbolic expression, reflecting how platforms standardize and algorithmically calibrate affect by smoothing out unpredictability in favor of predictability, efficiency, and control.

The third theme, negotiated authenticity and adaptive reception, describes how audiences in the discourse gradually recalibrate their expectations of AI-mediated features such as dubbing, machine translation, and algorithmic personalization. Simulated responses reflected a pragmatic shift in which convenience begins to outweigh concerns about cultural fidelity: viewers who once disliked dubbed content reported accepting AI dubbing tools such as Naver’s CLOVA Dubbing and Papago translation because they reduce friction, even when the translations occasionally produce awkward phrasing or flatten idioms, honorifics, and local humor. The author frames this as an adaptive willingness to trade emotional fidelity for convenience, novelty, and algorithmic accessibility, a process he terms algorithmic normalization.

The fourth theme addresses the trade-off between global reach and cultural specificity. The analysis suggests that Korean content is increasingly engineered for virality and universality through predictive scriptwriting, affective tagging, AI dubbing, and platform-optimized formatting, and that this often comes at the cost of originality, emotional texture, and local specificity. Simulated responses in the corpus expressed concern about cultural dilution, with content described as feeling like global templates rather than distinctly Korean works, and machine translation described as smoothing out the flavor of Korean speech. The author connects this to Iwabuchi’s earlier concept of cultural odorlessness, arguing that in the platform era odorlessness is no longer purely strategic but structurally imposed by the classificatory logics of metadata and engagement optimization.

The study’s theoretical argument is that these dynamics constitute a soft power paradox: algorithmic infrastructures amplify Korea’s cultural reach by optimizing content for virality, personalization, and translatability, yet in doing so they dilute cultural specificity and reshape the conditions of authenticity and symbolic depth. Soft power, in this framing, becomes less a deliberate instrument of cultural diplomacy and more an emergent property of algorithmic mediation, with platforms rather than policymakers orchestrating symbolic influence. The author also acknowledges significant limitations: the analysis is based on visible and circulating discourse rather than direct observation of user behavior or internal platform data; the materials are shaped by platform visibility and media amplification, which may privilege certain voices and regions; the study does not examine the institutional and technical processes through which these systems are designed and governed; and the framework is grounded primarily in the Korean context, limiting its generalizability. He positions Algorithmic Hallyu not as a closed empirical account but as a conceptual lens for examining how AI-mediated systems are reconfiguring the architecture of global media culture, and he calls for future research using large-scale surveys, diary studies, comparative cross-platform analyses, and investigation of the perspectives of platform designers, engineers, and policymakers.

Subject of Research: How algorithmic infrastructures reshape the global visibility, reception, and perceived authenticity of Korean popular culture

Article Title: How algorithms shape the global visibility of Korean content

Article References: Shin, D. (2026). How algorithms shape the global visibility of Korean content. Communication and Change, 2(1), Article 18. https://doi.org/10.1007/s44382-026-00035-2

Image Credits: AI Generated

DOI: 10.1007/s44382-026-00035-2

Keywords: Algorithmic Hallyu, Korean Wave, Hallyu, soft power, recommendation algorithms, generative AI, AI-generated idols, platform governance, cultural authenticity, Netflix, K-pop, qualitative discourse analysis