Learning Semantic-preserving Space Using User Profile and Multimodal Media Content from Political Social Network

Abstract

The use of social media in politics has dramatically changed the way campaigns are run and how elected officials interact with their constituents. An advanced algorithm is required to analyze and understand this large amount of heterogeneous social media data to investigate several key issues, such as stance and strategy, in political science. Most of previous works concentrate their studies using text-as-data approach, where the rich yet heterogeneous information in the user profile, social relationship, and multimodal media content is largely ignored. In this work, we propose a two-branch network that jointly maps the post contents and politician profile into the same latent space, which is trained using a large-margin objective that combines a cross-instance distance constraint with a within-instance semantic-preserving constraint. Our proposed political embedding space can be utilized not only in reliably identifying political spectrum and message type but also in providing a political representation space for interpretable ease-of-visualization.