What is the "filter bubble" effect and how does personalization contribute to it?

The "filter bubble" effect describes a situation where an individual's exposure to information is heavily filtered by algorithms that prioritize content aligning with their existing beliefs, interests, and past behavior. This results in a personalized information environment where they are primarily exposed to information that confirms their viewpoints, while dissenting or challenging information is filtered out. Personalization contributes to the filter bubble effect because algorithms use data about a user's preferences, search history, social connections, and browsing activity to tailor the content they see. While personalization aims to provide relevant and engaging content, it can inadvertently limit exposure to diverse perspectives and reinforce existing biases. This creates an echo chamber where individuals are less likely to encounter differing opinions or challenge their own assumptions, potentially leading to increased polarization and a limited understanding of the broader world. For example, social media platforms and search engines use personalization algorithms that can create filter bubbles, showing users content that aligns with their political views or consumer preferences, while filtering out alternative viewpoints.