| Summary: | This thesis examines the influence of social media recommendation algorithms on opinion polarization, focusing on how these systems curate content and amplify ideological divides. Agenda-setting theory which suggests that the media influences public perception by prioritizing certain issues, thereby shaping the public agenda and determining which topics are perceived as significant and deserving of attention has been used as a lens for the literature review. Recommendation algorithms, which enhance user experience by per-sonalizing content, simultaneously contribute to phenomena such as filter bubbles and echo chambers. These mechanisms isolate users in ideologically homogeneous environments, reinforcing existing beliefs and reducing exposure to diverse perspectives. Selective exposure and algorithmic curation further exacerbate polarization by amplifying emotionally charged and polarizing content, creating self-reinforcing cycles of division. The study reveals that while algorithms play a significant role in shaping user behaviour and public discourse, they are not the sole contributors to polarization. User engagement choices and broader social and psychological dynamics also contribute to the phenomenon. This thesis highlights the ethical challenges posed by these al-gorithms, such as the erosion of user autonomy and the prioritization of engagement over exposure diversity.
|
|---|