Algorithmic Polarisation on Social Media: Filter Bubbles and Echo Chambers

Abstract

This bachelor’s thesis investigates how social media platforms and their algorithmic systems contribute to polarisation especially among young users. The thesis analyses algorithms, filter bubbles and echo chambers and how these concepts interplay with each other forming the social media environment. The thesis utilises the social identity theory and the phenomenon of confirmation bias as a foundation for the research as they examine the underlying processes of polarisation. Moreover, the thesis highlights the significance of algorithmic literacy in today’s society. The purpose of the thesis was to provide insight and understanding into the role social media algorithms play in shaping polarisation.

A descriptive literature review was executed to explore existing academic research on algorithms and polarisation. The thesis was consists of 12 peer-reviewed academic articles with a specific focus on 6 as research material. The research question was: How do social media algorithms contribute to polarisation among young users.

The thesis concluded that algorithmic systems contribute to polarisation by structurally intensifying ideological uniformity, reinforcing selective exposure and limiting viewpoint diversity. Moreover, the results emphasised that algorithmic systems reinforce polarisation through content engagement. In addition, the results highlight that algorithmic systems can support the emergence of polarisation through underlying processes such as confirmation bias and social identity theory that shape the opinion formation process.