CULTURAL BAIT: KWAI’S COLD START ALGORITHM AND THE INSTRUMENTALIZATION OF BRAZILIAN CULTURE - Bitencourt, Elias Cunha; Santos, Guilherme Bispo C.; Oviedo, Nuasta; Batista, Rayssa Keuri Pereira; Bastos, Cecilio Ricardo de Carvalho (AoIR Selected Papers of Internet Research, 2026)
This study investigates how Kwai’s cold start algorithm instrumentalizes Brazilian culture as “cultural bait” to engineer user engagement and retention. Through computational analysis of Kwai’s cache-aware reinforcement learning (CARL) framework, we simulate four anonymous users in a cold start environment, collecting 4,000 posts. Using Vision Transformers (ViT), PCA, UMAP, and HDBSCAN clustering, we classify content into homogeneous, heterogeneous, and niche topics, validated via Jensen-Shannon divergence and Chi-square tests. Findings reveal 96.8% of cold start recommendations are homogenized, dominated by stereotypical themes like football (10.32%), telenovelas (12.65%), and suggestive humor (6.88%), alongside controversial clusters: misinformation (9.49%), Latin motivational content (9.35%), rural humor (8.25%), and violence clickbait (5.91%). This reflects Kwai’s reliance on cached, infrastructurally optimized cultural modules—shaped by bandwidth constraints and low-end devices markets where it targets—to prioritize computational efficiency and market scalability over personalization. We argue Kwai’s algorithmic epistemology operationalizes cultural bait: caricatured tropes repurposed as scalable, market-ready content, reducing culture to latent variables for knowledge speculation and user acquisition in emerging markets. By foregrounding computational constraints and cultural commodification, we demonstrate how algorithmic systems like CARL transform cultural experience into infrastructurally optimized data. These findings underscore analyzing algorithms not as black boxes or abstract entities but as politico-algebraic objects open to inquiry, where code encodes power asymmetries and cultural transformation. This urges media studies to bridge gaps between cultural critique and algebraic logic underpinning algorithmic epistemologies, avoiding treating these epistemologies as universal or generalizable, even among platforms operating within the same niche.