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
Venues:
ACL | IJCNLP | 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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2021.woah-1.4.pdf
Data
Hateful MemesHateful Memes Challenge