Breaking the bubble: A case study on the echo chamber effect in Instagram

The echo chamber effect on social media platforms, particularly Instagram, significantly influences users’ perception by creating environments where similar views are repeatedly reinforced. This case study explores the dynamics of the echo chamber effect on Instagram, focussing on the role of algorithmic filtering, user behaviour, and content personalisation. By analysing the platform’s mechanisms – such as personalised feeds, the Explore page, and targeted content – this study reveals how Instagram’s algorithm amplifies selective exposure and confirmation bias, leading to increased polarisation and reduced exposure to diverse viewpoints. The study examines psychological and social factors, including selective exposure theory, social identity, and emotional resonance, which intensify the echo chamber effect on a highly visual platform like Instagram. Findings suggest that this phenomenon can fragment social networks, create polarised communities, and contribute to the spread of misinformation. Strategies to mitigate these effects, including algorithmic transparency, diverse content exposure, and user-driven interactions, are discussed as potential methods for breaking echo chambers and promoting a more balanced informational ecosystem.