Intra-party politics has long been neglected due to lacking data sources. While we have a good understanding of the dynamics of ideological competition between parties, we know less about how individuals or groups inside parties influence policy, leadership selection and coalition bargaining. These questions can only be answered if we can place individual politicians and sub-party groups like factions on the same dimensions as in inter-party competition. This task has been notoriously difficult, as most existing measures either work on the party level, or are in other ways determined by the party agenda. Social media is a new data source that allows analyzing positions of individual politicians in party-centered systems, as it is subject to limited party control. I apply canonical correspondence analysis to account for hierarchical data structures and estimate multidimensional positions of the Twitter accounts of 498 Members of the German Bundestag based on more than 800,000 tweets since 2017. To test the effect of intra-party actors on their relative ideological placement, I coded the faction membership of 247 Twitter users in the Bundestag. I show that Twitter text reproduces party positions and dimensions. Members of factions are more likely to represent their faction’s positions, both on the cultural and the economic dimension.
Supplementary materials Replication Code and Data.