To cope with the immense amount of content on the web, search engines often use complex algorithms to personalize search results for individual users. However, personalization of search results has led to worries about the Filter Bubble Effect, where the personalization algorithm decides that some useful information is irrelevant to the user, and thus prevents them from locating it. In this paper, we propose a novel methodology to explore the impact of location-based personalization on Google Search results. Assessing the relationship between location and personalization is crucial, since users' geolocation can be used as a proxy for other demographic traits, like race, income, educational attainment, and political affiliation. In other words, does location-based personalization trap users in geolocal Filter Bubbles? Using our methodology, we collected 30 days of search results from Google Search in response to 240 different queries. By comparing search results gathered from 59 GPS coordinates around the US at three different granularities (county, state, and national), we are able to observe that differences in search results due to personalization grow as physical distance increases. However these differences are highly dependent on what a user searches for: queries for local establishments receive 4-5 different results per page, while more general terms exhibit essentially no personalization.
