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Hateful memes pose a unique challenge for current machine learning systemsbecause their message is derived from both text- and visual-modalities. To thiseffect, Facebook released the Hateful Memes Challenge, a dataset of memes withpre-extracted text captions, but it is unclear whether these synthetic examplesgeneralize to `memes in the wild'. In this paper, we collect hateful andnon-hateful memes from Pinterest to evaluate out-of-sample performance onmodels pre-trained on the Facebook dataset. We find that memes in the wilddiffer in two key aspects: 1) Captions must be extracted via OCR, injectingnoise and diminishing performance of multimodal models, and 2) Memes are morediverse than `traditional memes', including screenshots of conversations ortext on a plain background. This paper thus serves as a reality check for thecurrent benchmark of hateful meme detection and its applicability for detectingreal world hate.

Memes in the Wild: Assessing the Generalizability of the Hateful Memes Challenge Dataset