# What is Beneath Misogyny: Misogynous Memes Classification and Explanation

arXiv: https://arxiv.org/abs/2508.03732 · HTML: https://mlsanity.com/paper/2508.03732/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 30 Jul 2025.

## Builds on (most cited first, 5 of 5)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [MemeCap: A Dataset for Captioning and Interpreting Memes](https://mlsanity.com/paper/2305.13703/) (2023)
- [Revisiting Instruction Fine-tuned Model Evaluation to Guide Industrial Applications](https://mlsanity.com/paper/2310.14103/) (2023)
- [Contextualizing Internet Memes Across Social Media Platforms](https://mlsanity.com/paper/2311.11157/) (2023)

## Abstract (arXiv)

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, textitnamely, textitMM-Misogyny to detect, categorize, and explain misogynistic content in memes. textitMM-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 (textitWhat's Beneath Misogynous Stereotyping (WBMS)) created by collecting misogynous memes from cyberspace and categorizing them into four categories, textitnamely, 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 hrefhttps://github.com/kushalkanwarNS/WhatisBeneathMisogyny/tree/mainhttps://github.com/Misogyny.