Enhancing joint ideal point estimation strategies with Twitter social networks and text data

Analyses of online social interactions on major platforms have become central to evaluating the ideological leanings of users. Social scientists often represent individuals’ policy preferences as ideal points in a low-dimensional space. We first demonstrate that standard ideal-point estimation approaches can be temporally inconsistent by examining the evolution of ideological positions for political elites and non-elite Twitter users from 2017 to 2022. To address this instability and to improve overall measurement quality, we augment the social-network data conventionally used for ideal-point estimation with unstructured text data widely available on social media. Our results indicate that this data-augmented approach improves identification at the extreme ends of the ideological spectrum and yields robust estimates consistent with those from more complex word-embedding techniques, while offering greater computational efficiency and interpretability. This framework can enhance future studies that measure political ideology using behavioral data and inform broader discussions about integrating textual and network information.