  Learning social media content is the basis of many real-world applications,
including information retrieval and recommendation systems, among others. In
contrast with previous works that focus mainly on single modal or bi-modal
learning, we propose to learn social media content by fusing jointly textual,
acoustic, and visual information (JTAV). Effective strategies are proposed to
extract fine-grained features of each modality, that is, attBiGRU and DCRNN. We
also introduce cross-modal fusion and attentive pooling techniques to integrate
multi-modal information comprehensively. Extensive experimental evaluation
conducted on real-world datasets demonstrates our proposed model outperforms
the state-of-the-art approaches by a large margin.
