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

JTAV: Jointly Learning Social Media Content Representation by Fusing Textual, Acoustic, and Visual Features