Auditing the Personalization and Composition of Politically-Related Search Engine Results Pages

Search engines are a primary means through which people obtain information in today's connected world. Yet, apart from the search engine companies themselves, little is known about how their algorithms filter, rank, and present the web to users. This question is especially pertinent with respect to political queries, given growing concerns about filter bubbles, and the recent finding that bias or favoritism in search rankings can influence voting behavior. In this study, we conduct a targeted algorithm audit of Google Search using a dynamic set of political queries. We designed a Chrome extension to survey participants and collect the Search Engine Results Pages (SERPs) and autocomplete suggestions that they would have been exposed to while searching our set of political queries during the month after Donald Trump's Presidential inauguration. Using this data, we found significant differences in the composition and personalization of politically-related SERPs by query type, subjects' characteristics, and date.