We presented a comprehensive audit of algorithmic amplification of political content by the recommender system in Twitter’s home timeline. Across seven countries we studied, we found that mainstream right-wing parties benefit at least as much, and often substantially more, from algorithmic personalization as their left-wing counterparts. In agreement with this, we found that content from U.S. media outlets with a strong right-leaning bias are amplified marginally more than content from left-leaning sources. However, when making comparisons based on the amplification of individual politician’s accounts, rather than parties in aggregate, we found no association between amplification and party membership.
Our analysis of far-left and far-right parties in various countries does not support the hypothesis that algorithmic personalization amplifies extreme ideologies more than mainstream political voices. However, some findings point at the possibility that strong partisan bias in news reporting is associated with higher amplification. We note that strong partisan bias here means a consistent tendency to report news in a way favouring one party or another, and does not imply the promotion of extreme political ideology.