  Political leaning can be defined as the inclination of an individual towards
certain political orientations that align with their personal beliefs.
Political leaning inference has traditionally been framed as a binary
classification problem, namely, to distinguish between left vs. right or
conservative vs liberal. Furthermore, although some recent work considers
political leaning inference in a multi-party multi-region framework, their
study is limited to the application of social interaction data. In order to
address these shortcomings, in this study we propose Hybrid Text-Interaction
Modeling (HTIM), a framework that enables hybrid modeling fusioning text and
interactions from Social Media to accurately identify the political leaning of
users in a multi-party multi-region framework. Access to textual and
interaction-based data not only allows us to compare these data sources but
also avoids reliance on specific data types. We show that, while
state-of-the-art text-based representations on their own are not able to
improve over interaction-based representations, a combination of text-based and
interaction-based modeling using HTIM considerably improves the performance
across the three regions, an improvement that is more prominent when we focus
on the most challenging cases involving users who are less engaged in politics.
