Algorithmic Culture: How Social Media Recommendation Systems Shape Collective Communication Norms

Abstract

This study aims to examine how social media recommendation systems shape collective communication norms within contemporary digital culture. It employs a qualitative method with an interpretive digital case study design, as this approach is best suited to capturing the relationship between algorithmic logic, communicative practice, and the production of social meaning across platform environments. The research is situated in the digital ecology of TikTok and Instagram, with data collection conducted in Jakarta, Indonesia, a metropolitan setting characterized by intensive social media use and a diverse range of digital communication actors. The study involved twelve informants selected purposively on the basis of their experience, role, and sustained engagement in algorithmically curated communication environments, enabling the researcher to obtain rich and relevant qualitative insight. The findings indicate that recommendation systems do more than organize content distribution; they normalize specific communicative forms, especially those that are brief, emotional, visual, rapid, and engagement-oriented. These results confirm that algorithms function as cultural agents shaping standards of public expression. The study recommends strengthening algorithmic literacy, improving platform transparency, and promoting more ethical digital communication practices.