Selective exposure theory tells us that individuals actively seek information that pertains specifically to pre-existing beliefs while avoiding information that may have contradictions; this leads to a political polarization in a democratic society. Consequently, there are increasing concerns regarding the personalization algorithms of search engines and social media limiting diverse viewpoints and feeding into a person's biases beyond a user's control. This study aims to understand whether a user's online activity influences personalization algorithms on Google News searches to return stories that are reinforcing an individual's racial-political biases. To examine this issue, a multi-stage "sock puppet" algorithm audit method was employed. Developing three programmed computer bots, each bot visited 50 URLs on two Twitter accounts that reflects bipolar radical-political rhetoric; the bots then conducted identical politically oriented Google News searches; after receiving all the search results, the search returns were then all compared to test for personalization and political biases Preliminary results found that google News, based on web history that reflects racial-political discourses, does personalize search results Currently, there is an increasing knowledge regarding the personalization of algorithm on search engines. This study builds upon the current literature on the impact of search results of race and representation on the online user by investigating not only the search results but the algorithmic process of the input as well - specifically, a web history that reflects a racialized political discourse community.