This is a project for understanding and quantifying information filter bubbles (http://www.thefilterbubble.com), using a distributed measurement infrastructure like Bismark.
Filter bubbles focus on the impact of web personalization based on regional and personal characteristics. A user is exposed to things that he will probably like and be interested to. This creates a virtual environment which looks very familiar and friendly, removing "irrelevant" and/or "unpopular" content, where relevance and popularity is based on a rich vector of personal, regional, technical characteristics, and limits the breadth of user's experience.
The argument so far is on a relatively high-level. A more systematic and detailed study could help us understand and quantify how these reflects to the experience users perceive.
Using a distributed measurement platform like Bismark we can generate identical search requests from different regions to study such behaviors.