Related papers: Text or Image? What is More Important in Cross-Domain Generalization Capabilities of Hate Meme Detection Models?

Text or Image? What is More Important in Cross-Domain Generalization Capabilities of Hate Meme Detection Models?

URL: http://arxiv.org/abs/2402.04967v1
Date: Wed, 7 Feb 2024 15:44:55 GMT
Title: Text or Image? What is More Important in Cross-Domain Generalization Capabilities of Hate Meme Detection Models?
Authors: Piush Aggarwal, Jawar Mehrabanian, Weigang Huang, \"Ozge Alacam and Torsten Zesch
Abstract summary: This paper delves into the formidable challenge of cross-domain generalization in multimodal hate meme detection. We provide enough pieces of evidence supporting the hypothesis that only the textual component of hateful memes enables the existing multimodal classifier to generalize across different domains. Our evaluation on a newly created confounder dataset reveals higher performance on text confounders as compared to image confounders with an average $Delta$F1 of 0.18.
Score: 2.4899077941924967
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: This paper delves into the formidable challenge of cross-domain generalization in multimodal hate meme detection, presenting compelling findings. We provide enough pieces of evidence supporting the hypothesis that only the textual component of hateful memes enables the existing multimodal classifier to generalize across different domains, while the image component proves highly sensitive to a specific training dataset. The evidence includes demonstrations showing that hate-text classifiers perform similarly to hate-meme classifiers in a zero-shot setting. Simultaneously, the introduction of captions generated from images of memes to the hate-meme classifier worsens performance by an average F1 of 0.02. Through blackbox explanations, we identify a substantial contribution of the text modality (average of 83%), which diminishes with the introduction of meme's image captions (52%). Additionally, our evaluation on a newly created confounder dataset reveals higher performance on text confounders as compared to image confounders with an average $\Delta$F1 of 0.18.