What is Beneath Misogyny: Misogynous Memes Classification and Explanation

• URL: http://arxiv.org/abs/2508.03732v1
• Date: Wed, 30 Jul 2025 14:38:53 GMT
• Title: What is Beneath Misogyny: Misogynous Memes Classification and Explanation
• Authors: Kushal Kanwar, Dushyant Singh Chauhan, Gopendra Vikram Singh, Asif Ekbal,
• Abstract summary: We introduce a novel approach to detect, categorize, and explain misogynistic content in memes.<n>textitnamely, textittextbfMM-Misogyny processes text and image modalities separately.<n>The model not only detects and classifies misogyny, but also provides a granular understanding of how misogyny operates in domains of life.
• Score: 20.78432772119578
• License: http://creativecommons.org/licenses/by/4.0/
• 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}.

Related papers

• MemeReaCon: Probing Contextual Meme Understanding in Large Vision-Language Models [50.2355423914562]
  We introduce MemeReaCon, a novel benchmark designed to evaluate how Large Vision Language Models (LVLMs) understand memes in their original context.<n>We collected memes from five different Reddit communities, keeping each meme's image, the post text, and user comments together.<n>Our tests with leading LVLMs show a clear weakness: models either fail to interpret critical information in the contexts, or overly focus on visual details while overlooking communicative purpose.
  arXiv  Detail & Related papers  (2025-05-23T03:27:23Z)
• Language is Scary when Over-Analyzed: Unpacking Implied Misogynistic Reasoning with Argumentation Theory-Driven Prompts [17.259767031006604]
  We propose misogyny detection as an Argumentative Reasoning task. We investigate the capacity of large language models to understand the implicit reasoning used to convey misogyny in both Italian and English.
  arXiv  Detail & Related papers  (2024-09-04T08:27:43Z)
• XMeCap: Meme Caption Generation with Sub-Image Adaptability [53.2509590113364]
  Humor, deeply rooted in societal meanings and cultural details, poses a unique challenge for machines.<n>We introduce the textscXMeCap framework, which adopts supervised fine-tuning and reinforcement learning.<n>textscXMeCap achieves an average evaluation score of 75.85 for single-image memes and 66.32 for multi-image memes, outperforming the best baseline by 6.75% and 8.56%, respectively.
  arXiv  Detail & Related papers  (2024-07-24T10:51:46Z)
• A multitask learning framework for leveraging subjectivity of annotators to identify misogyny [47.175010006458436]
  We propose a multitask learning approach to enhance the performance of the misogyny identification systems. We incorporated diverse perspectives from annotators in our model design, considering gender and age across six profile groups. This research advances content moderation and highlights the importance of embracing diverse perspectives to build effective online moderation systems.
  arXiv  Detail & Related papers  (2024-06-22T15:06:08Z)
• Meme-ingful Analysis: Enhanced Understanding of Cyberbullying in Memes Through Multimodal Explanations [48.82168723932981]
  We introduce em MultiBully-Ex, the first benchmark dataset for multimodal explanation from code-mixed cyberbullying memes. A Contrastive Language-Image Pretraining (CLIP) approach has been proposed for visual and textual explanation of a meme.
  arXiv  Detail & Related papers  (2024-01-18T11:24:30Z)
• UPB at SemEval-2022 Task 5: Enhancing UNITER with Image Sentiment and Graph Convolutional Networks for Multimedia Automatic Misogyny Identification [0.3437656066916039]
  We describe our classification systems submitted to the SemEval-2022 Task 5: MAMI - Multimedia Automatic Misogyny Identification. Our best model reaches an F1-score of 71.4% in Sub-task A and 67.3% for Sub-task B positioning our team in the upper third of the leaderboard.
  arXiv  Detail & Related papers  (2022-05-29T21:12:36Z)
• DisinfoMeme: A Multimodal Dataset for Detecting Meme Intentionally Spreading Out Disinformation [72.18912216025029]
  We present DisinfoMeme to help detect disinformation memes. The dataset contains memes mined from Reddit covering three current topics: the COVID-19 pandemic, the Black Lives Matter movement, and veganism/vegetarianism.
  arXiv  Detail & Related papers  (2022-05-25T09:54:59Z)
• DISARM: Detecting the Victims Targeted by Harmful Memes [49.12165815990115]
  DISARM is a framework that uses named entity recognition and person identification to detect harmful memes. We show that DISARM significantly outperforms ten unimodal and multimodal systems. It can reduce the relative error rate for harmful target identification by up to 9 points absolute over several strong multimodal rivals.
  arXiv  Detail & Related papers  (2022-05-11T19:14:26Z)
• Detecting and Understanding Harmful Memes: A Survey [48.135415967633676]
  We offer a comprehensive survey with a focus on harmful memes. One interesting finding is that many types of harmful memes are not really studied, e.g., such featuring self-harm and extremism. Another observation is that memes can propagate globally through repackaging in different languages and that they can also be multilingual.
  arXiv  Detail & Related papers  (2022-05-09T13:43:27Z)
• TIB-VA at SemEval-2022 Task 5: A Multimodal Architecture for the Detection and Classification of Misogynous Memes [9.66022279280394]
  We present a multimodal architecture that combines textual and visual features in order to detect misogynous meme content. Our solution obtained the best result in the Task-B where the challenge is to classify whether a given document is misogynous.
  arXiv  Detail & Related papers  (2022-04-13T11:03:21Z)
• RubCSG at SemEval-2022 Task 5: Ensemble learning for identifying misogynous MEMEs [12.979213013465882]
  This work presents an ensemble system based on various uni-modal and bi-modal model architectures developed for the SemEval 2022 Task 5: MAMI-Multimedia Automatic Misogyny Identification.
  arXiv  Detail & Related papers  (2022-04-08T09:27:28Z)
• Memes in the Wild: Assessing the Generalizability of the Hateful Memes Challenge Dataset [47.65948529524281]
  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, and 2) Memes are more diverse than traditional memes', including screenshots of conversations or text on a plain background.
  arXiv  Detail & Related papers  (2021-07-09T09:04:05Z)
• Benchmark dataset of memes with text transcriptions for automatic detection of multi-modal misogynistic content [0.8261182037130405]
  dataset is composed of 800 memes collected from the most popular social media platforms. Experts have selected a dataset of 800 memes equally balanced between misogynistic and non-misogynistic ones. This data can be used to approach the problem of automatic detection of misogynistic content on the Web.
  arXiv  Detail & Related papers  (2021-06-15T20:01:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.