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

Hateful memes pose a unique challenge for current machine learning systems because their message is derived from both text- and visual-modalities. To this effect, Facebook released the Hateful Memes Challenge, a dataset of memes with pre-extracted text captions, but it is unclear whether these synth...

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Hauptverfasser: Kirk, Hannah Rose, Jun, Yennie, Rauba, Paulius, Wachtel, Gal, Li, Ruining, Bai, Xingjian, Broestl, Noah, Doff-Sotta, Martin, Shtedritski, Aleksandar, Asano, Yuki M
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Sprache:eng
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Zusammenfassung:Hateful memes pose a unique challenge for current machine learning systems because their message is derived from both text- and visual-modalities. To this effect, Facebook released the Hateful Memes Challenge, a dataset of memes with pre-extracted text captions, but it is unclear whether these synthetic examples generalize to `memes in the wild'. In this paper, we collect hateful and non-hateful memes from Pinterest to evaluate out-of-sample performance on models pre-trained on the Facebook dataset. We find that memes in the wild differ in two key aspects: 1) Captions must be extracted via OCR, injecting noise and diminishing performance of multimodal models, and 2) Memes are more diverse than `traditional memes', including screenshots of conversations or text on a plain background. This paper thus serves as a reality check for the current benchmark of hateful meme detection and its applicability for detecting real world hate.
DOI:10.48550/arxiv.2107.04313