Memes in the Wild: Assessing the Generalizability of the Hateful Memes Challenge Dataset
Hannah Kirk, Yennie Jun, Paulius Rauba, Gal Wachtel, Ruining Li, Xingjian Bai, Noah Broestl, Martin Doff-Sotta, Aleksandar Shtedritski, Yuki M Asano
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.- Anthology ID:
- 2021.woah-1.4
- Volume:
- Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Aida Mostafazadeh Davani, Douwe Kiela, Mathias Lambert, Bertie Vidgen, Vinodkumar Prabhakaran, Zeerak Waseem
- Venue:
- WOAH
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 26–35
- Language:
- URL:
- https://aclanthology.org/2021.woah-1.4
- DOI:
- 10.18653/v1/2021.woah-1.4
- Cite (ACL):
- Hannah Kirk, Yennie Jun, Paulius Rauba, Gal Wachtel, Ruining Li, Xingjian Bai, Noah Broestl, Martin Doff-Sotta, Aleksandar Shtedritski, and Yuki M Asano. 2021. Memes in the Wild: Assessing the Generalizability of the Hateful Memes Challenge Dataset. In Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021), pages 26–35, Online. Association for Computational Linguistics.
- Cite (Informal):
- Memes in the Wild: Assessing the Generalizability of the Hateful Memes Challenge Dataset (Kirk et al., WOAH 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2021.woah-1.4.pdf
- Data
- Hateful Memes, Hateful Memes Challenge