• 1 Waseda University

Social media platforms, such as Twitter (now X), play a crucial role during crises by enabling real-time information sharing. However, the multimodal data can be ambiguous with misalignment of labels cross-modality. Being able to classify informative and not informative tweets can help in crisis response, yet they can be ambiguous and unbalanced in datasets, impairing model performance. This study explores the effectiveness of multimodal learning approaches for classifying crisis-related tweets regardless of ambiguity and addressing class imbalance through synthetic data augmentation using generative artificial intelligence (AI). Experimental results demonstrate that multimodal models consistently outperform unimodal ones, particularly on ambiguous tweets where label misalignment between modalities is prevalent. Furthermore, the addition of synthetic data significantly boosts macro F1 scores, indicating improved performance on the minority class.