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Computing author intent from multimodal data like Instagram posts requiresmodeling a complex relationship between text and image. For example, a captionmight evoke an ironic contrast with the image, so neither caption nor image isa mere transcript of the other. Instead they combine -- via what has beencalled meaning multiplication -- to create a new meaning that has a morecomplex relation to the literal meanings of text and image. Here we introduce amultimodal dataset of 1299 Instagram posts labeled for three orthogonaltaxonomies: the authorial intent behind the image-caption pair, the contextualrelationship between the literal meanings of the image and caption, and thesemiotic relationship between the signified meanings of the image and caption.We build a baseline deep multimodal classifier to validate the taxonomy,showing that employing both text and image improves intent detection by 9.6%compared to using only the image modality, demonstrating the commonality ofnon-intersective meaning multiplication. The gain with multimodality isgreatest when the image and caption diverge semiotically. Our dataset offers anew resource for the study of the rich meanings that result from pairing textand image.

Integrating Text and Image: Determining Multimodal Document Intent in Instagram Posts