Algorithm-Driven Identity Politics: Role of Social Media in Polarisation and Mobilisation

Abstract

This is an essay that explores the role of recommendation algorithms in the communication of identity politics and their sociological and political implications, focusing on how the phenomena of filter bubbles and information cocoons reshape users' information exposure and cognitive structures. By combining Baudrillard's theory of mimetic environments, Bourdieu's theory of cultural capital, and Mead's theory of symbolic interaction, the article reveals how recommender algorithms reinforce group identities and exacerbate social divisions through selective pushing of information. In addition, the proliferation of disinformation exhibits higher dissemination efficiency in algorithm-driven environments, posing challenges to the openness and inclusiveness of the public sphere. The article calls for multi-dimensional interventions in technological design, policy regulation and public education to mitigate the masking effect of algorithmic communication and the negative impact of disinformation, and to provide solution paths for building a pluralistic and open digital public sphere. This article aims to provide theoretical support for understanding the double-edged sword effect of recommendation algorithms in identity politics and practical suggestions for the governance and optimisation of digital society.