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Multimedia Modeling
Audience
Other: Media Processing for Interpretation
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.
Figures
The illustration of our proposed political semantic space network.
The illustration of our proposed political semantic space network.
Keywords
semantic-preserving space | multimodal media data | social media | politics | large-margin objective
Authors
Publication Date
2019/05/12
Conference
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
DOI
10.1109/icassp.2019.8682596
Publisher
IEEE