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Prior work on ideology prediction has largely focused on single modalities,i.e., text or images. In this work, we introduce the task of multimodalideology prediction, where a model predicts binary or five-point scaleideological leanings, given a text-image pair with political content. We firstcollect five new large-scale datasets with English documents and images alongwith their ideological leanings, covering news articles from a wide range of USmainstream media and social media posts from Reddit and Twitter. We conductin-depth analyses of news articles and reveal differences in image content andusage across the political spectrum. Furthermore, we perform extensiveexperiments and ablation studies, demonstrating the effectiveness of targetedpretraining objectives on different model components. Our best-performingmodel, a late-fusion architecture pretrained with a triplet objective overmultimodal content, outperforms the state-of-the-art text-only model by almost4% and a strong multimodal baseline with no pretraining by over 3%.

Late Fusion with Triplet Margin Objective for Multimodal Ideology Prediction and Analysis