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We report on the outcome of an audit of Twitter's Home Timeline rankingsystem. The goal of the audit was to determine if authors from some racialgroups experience systematically higher impression counts for their Tweets thanothers. A central obstacle for any such audit is that Twitter does notordinarily collect or associate racial information with its users, thusprohibiting an analysis at the level of individual authors. Working around thisobstacle, we take US counties as our unit of analysis. We associate each userin the United States on the Twitter platform to a county based on availablelocation data. The US Census Bureau provides information about the racialdecomposition of the population in each county. The question we investigatethen is if the racial decomposition of a county is associated with thevisibility of Tweets originating from within the county. Focusing on two racialgroups, the Black or African American population and the White population asdefined by the US Census Bureau, we evaluate two statistical measures of bias.Our investigation represents the first large-scale algorithmic audit intoracial bias on the Twitter platform. Additionally, it illustrates thechallenges of measuring racial bias in online platforms without having suchinformation on the users.

County-level Algorithmic Audit of Racial Bias in Twitter's Home Timeline