HTIM: Hybrid Text-Interaction Modeling for Broadening Political Leaning Inference in Social Media

TL;DR

This paper introduces HTIM, a hybrid framework combining social media text and interaction data to improve political leaning inference across multiple regions, especially for less engaged users.

Contribution

The study proposes a novel hybrid modeling framework, HTIM, that fuses text and interaction data for more accurate multi-region political leaning inference.

Findings

Hybrid modeling improves inference accuracy significantly.

Text data alone does not outperform interaction data.

Performance gains are notable for less engaged users.

Abstract

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.