What is Beneath Misogyny: Misogynous Memes Classification and Explanation

TL;DR

This paper presents MM-Misogyny, a novel multimodal model that detects, categorizes, and explains misogynistic memes by analyzing image and text content, providing insights into how misogyny manifests across different societal domains.

Contribution

The paper introduces a new multimodal approach using cross-attention and LLMs for misogyny detection and explanation, along with a curated dataset WBMS for evaluation.

Findings

Outperforms existing methods in misogyny detection accuracy

Provides detailed explanations of misogynistic content

Effectively categorizes memes into societal domains

Abstract

Memes are popular in the modern world and are distributed primarily for entertainment. However, harmful ideologies such as misogyny can be propagated through innocent-looking memes. The detection and understanding of why a meme is misogynous is a research challenge due to its multimodal nature (image and text) and its nuanced manifestations across different societal contexts. We introduce a novel multimodal approach, \textit{namely}, \textit{\textbf{MM-Misogyny}} to detect, categorize, and explain misogynistic content in memes. \textit{\textbf{MM-Misogyny}} processes text and image modalities separately and unifies them into a multimodal context through a cross-attention mechanism. The resulting multimodal context is then easily processed for labeling, categorization, and explanation via a classifier and Large Language Model (LLM). The evaluation of the proposed model is performed on a newly curated dataset (\textit{\textbf{W}hat's \textbf{B}eneath \textbf{M}isogynous \textbf{S}tereotyping (WBMS)}) created by collecting misogynous memes from cyberspace and categorizing them into four categories, \textit{namely}, Kitchen, Leadership, Working, and Shopping. The model not only detects and classifies misogyny, but also provides a granular understanding of how misogyny operates in domains of life. The results demonstrate the superiority of our approach compared to existing methods. The code and dataset are available at \href{https://github.com/kushalkanwarNS/WhatisBeneathMisogyny/tree/main}{https://github.com/Misogyny}.