  We report on the outcome of an audit of Twitter's Home Timeline ranking
system. The goal of the audit was to determine if authors from some racial
groups experience systematically higher impression counts for their Tweets than
others. A central obstacle for any such audit is that Twitter does not
ordinarily collect or associate racial information with its users, thus
prohibiting an analysis at the level of individual authors. Working around this
obstacle, we take US counties as our unit of analysis. We associate each user
in the United States on the Twitter platform to a county based on available
location data. The US Census Bureau provides information about the racial
decomposition of the population in each county. The question we investigate
then is if the racial decomposition of a county is associated with the
visibility of Tweets originating from within the county. Focusing on two racial
groups, the Black or African American population and the White population as
defined by the US Census Bureau, we evaluate two statistical measures of bias.
Our investigation represents the first large-scale algorithmic audit into
racial bias on the Twitter platform. Additionally, it illustrates the
challenges of measuring racial bias in online platforms without having such
information on the users.
