  Internet memes are characterised by the interspersing of text amongst visual
elements. State-of-the-art multimodal meme classifiers do not account for the
relative positions of these elements across the two modalities, despite the
latent meaning associated with where text and visual elements are placed.
Against two meme sentiment classification datasets, we systematically show
performance gains from incorporating the spatial position of visual objects,
faces, and text clusters extracted from memes. In addition, we also present
facial embedding as an impactful enhancement to image representation in a
multimodal meme classifier. Finally, we show that incorporating this spatial
information allows our fully automated approaches to outperform their
corresponding baselines that rely on additional human validation of
OCR-extracted text.
