Data heterogeneity is a fundamental issue in social intelligence, where the data is often obtained from diverse sources, modalities, and contexts, due to the varying nature of human interactions and behaviors. Addressing the data heterogeneity problem in social intelligence encounters several unique challenges, such as cross-modal information inconsistency, sparse multimodal data annotation, and heterogeneous feature fusion. To overcome these challenges, this chapter reviews state-of-the-art multimodal solutions that address the data heterogeneity challenge in social intelligence applications. In particular, we present two case studies: one on generative learning based multimodal truth discovery and another on contrastive learning based fauxtography detection. These case studies demonstrate the superiority and potential of advanced deep learning techniques in addressing data heterogeneity issues in social intelligence tasks, paving the way for more accurate and reliable analysis of diverse data modalities.