What is Beneath Misogyny: Misogynous Memes Classification and Explanation
Kushal Kanwar, Dushyant Singh Chauhan, Gopendra Vikram Singh, Asif Ekbal

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…
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