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

Hannah Rose Kirk; Yennie Jun; Paulius Rauba; Gal Wachtel; Ruining Li; Xingjian Bai; Noah Broestl; Martin Doff-Sotta; Aleksandar Shtedritski; Yuki M. Asano

arXiv · 2107.04313

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

Hannah Rose Kirk·Yennie Jun·Paulius Rauba·Gal Wachtel·Ruining Li·Xingjian Bai·Noah Broestl·Martin Doff-Sotta·Aleksandar Shtedritski·Yuki M. Asano

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Abstract

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.

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BibTeX RIS

Hannah Rose Kirk, Yennie Jun, Paulius Rauba, Gal Wachtel, Ruining Li, Xingjian Bai, Noah Broestl, Martin Doff-Sotta, Aleksandar Shtedritski, Yuki M. Asano. 2021-07-09. Memes in the Wild: Assessing the Generalizability of the Hateful Memes Challenge Dataset. https://arxiv.org/abs/2107.04313

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