This thesis aims to explore and describe the content diversity of Siri’s search results in the polarized context of US politics. To do so, a crowdsourced audit was conducted. A diverse sample of 134 US-based Siri users between the ages of 18-64 performed five identical queries about the politically controversial issues of gun laws, immigration, the death penalty, taxes and abortion. The data were viewed through a theoretical framework using the concepts of algorithmic bias and media-centric fragmentation. The results suggest that Siri’s search algorithm produces a long tail distribution of search results: Forty-two percent of the participants received the six most frequent answers, while 22% of the users received unique answers. These statistics indicate that Siri’s search algorithm causes moderate concentration and low fragmentation. The age and, surprisingly, the political orientation of users, do not seem to be driving either concentration or fragmentation. However, the users' gender and location appears to cause low concentration. The finding that Siri’s search algorithm produces a long tail of replies challenges previous research on the content diversity of search results, which found no evidence of fragmentation. However, due to the limited scope of this study, these findings cannot be generalized to a larger population. Further research is needed to support or refute them.