Political scientists increasingly analyze multimodal data. However, the effective analysis of such data requires aligning information across different modalities. In our paper, we demonstrate the significance of such alignment. Informed by a systematic review of 2,703 papers, we find that political scientists typically do not align their multimodal data. Introducing a decision tree that guides alignment choices, our framework highlights alignment's untapped potential and provides concrete advice in research design and modeling decisions. We illustrate alignment's analytical value through two applications: predicting tonality in U.S. presidential campaign ads and cross-modal querying of German parliamentary speeches to examine responses to the far-right AfD.
@article{arxiv.2405.08454,
title = {Alignment Helps Make the Most of Multimodal Data},
author = {Christian Arnold and Andreas Küpfer},
journal= {arXiv preprint arXiv:2405.08454},
year = {2025}
}