Algorithmic Influence and Cultural Narratives in the Digital Public Sphere

Chen Jie, Wang Jun
DOI: http://dx.doi.org/10.19085/sijmas110201

Abstract

This paper examines how recommendation systems, content moderation protocols, and platform architectures shape cultural narratives in digital communication spaces. Drawing on Habermas's public sphere theory and more recent scholarship in platform studies, critical data studies, and the political economy of media, the paper argues that algorithmic systems do not merely distribute content but actively construct the conditions under which cultural meaning is produced and contested. Three interrelated processes are examined: the personalization of information environments, the amplification of emotionally charged content, and the commercial logic of attention capture. Together, these processes produce a fragmented cultural landscape where the conditions for sustained public discourse are increasingly difficult to maintain. The paper further examines how surveillance capitalism, as theorized by Zuboff (2019), intensifies algorithmic influence by converting behavioral data into predictive instruments that shape what audiences see, believe, and value. The analysis includes a comparative framework contrasting the normative structure of Habermas's public sphere with the operating conditions of the contemporary algorithmic public sphere. The paper concludes by calling for regulatory responses, including platform transparency obligations and algorithmic accountability measures, that acknowledge the structural power of platform companies without foreclosing the possibilities for user agency and counter-narrative production.

Keywords

Algorithms, cultural narratives, digital public sphere, filter bubbles, platform studies, surveillance capitalism

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