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.
CCS CONCEPTS
INTRODUCTIONRecent concerns surrounding political polarization, fake news, and the impact of media on public opinion have largely focused on social media platforms like Facebook and Twitter. Yet, recent surveys suggest that more news is sought through search engines than social media [2,57], and that search engines are the secondmost likely news gateway to inspire follow-up actions, such as This paper is published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Authors reserve their rights to disseminate the work on their personal and corporate Web sites with the appropriate attribution.