# On the Evolution of (Hateful) Memes by Means of Multimodal Contrastive Learning

arXiv: https://arxiv.org/abs/2212.06573 · HTML: https://mlsanity.com/paper/2212.06573/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 69 references that are in the map.
- First posted on arXiv 13 Dec 2022.

## Builds on (most cited first, 23 of 23)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes](https://mlsanity.com/paper/2005.04790/) (2020)
- [Detecting and Understanding Harmful Memes: A Survey](https://mlsanity.com/paper/2205.04274/) (2022)
- [Auditing Radicalization Pathways on YouTube](https://mlsanity.com/paper/1908.08313/) (2019)
- [Vilio: State-of-the-art Visio-Linguistic Models applied to Hateful Memes](https://mlsanity.com/paper/2012.07788/) (2020)
- [On the Origins of Memes by Means of Fringe Web Communities](https://mlsanity.com/paper/1805.12512/) (2018)
- [Enhance Multimodal Transformer With External Label And In-Domain Pretrain: Hateful Meme Challenge Winning Solution](https://mlsanity.com/paper/2012.08290/) (2020)
- [Hate Speech in Pixels: Detection of Offensive Memes towards Automatic Moderation](https://mlsanity.com/paper/1910.02334/) (2019)
- [A Quantitative Approach to Understanding Online Antisemitism](https://mlsanity.com/paper/1809.01644/) (2018)
- [Raiders of the Lost Kek: 3.5 Years of Augmented 4chan Posts from the Politically Incorrect Board](https://mlsanity.com/paper/2001.07487/) (2020)
- [Quarantined! Examining the Effects of a Community-Wide Moderation Intervention on Reddit](https://mlsanity.com/paper/2009.11483/) (2020)
- [Understanding and Detecting Hateful Content using Contrastive Learning](https://mlsanity.com/paper/2201.08387/) (2022)
- [Predicting Successful Memes using Network and Community Structure](https://mlsanity.com/paper/1403.6199/) (2014)
- [MemeSequencer: Sparse Matching for Embedding Image Macros](https://mlsanity.com/paper/1802.04936/) (2018)
- ["And We Will Fight For Our Race!" A Measurement Study of Genetic Testing Conversations on Reddit and 4chan](https://mlsanity.com/paper/1901.09735/) (2019)
- ["I Won the Election!": An Empirical Analysis of Soft Moderation Interventions on Twitter](https://mlsanity.com/paper/2101.07183/) (2021)
- [On Xing Tian and the Perseverance of Anti-China Sentiment Online](https://mlsanity.com/paper/2204.08935/) (2022)
- ["You Know What to Do": Proactive Detection of YouTube Videos Targeted by Coordinated Hate Attacks](https://mlsanity.com/paper/1805.08168/) (2018)
- [A Comparison of Online Hate on Reddit and 4chan: A Case Study of the 2020 US Election](https://mlsanity.com/paper/2202.01302/) (2022)

## Built on by (most cited first, 26 of 26)

- [Unsafe Diffusion: On the Generation of Unsafe Images and Hateful Memes From Text-To-Image Models](https://mlsanity.com/paper/2305.13873/) (2023)
- [UnsafeBench: Benchmarking Image Safety Classifiers on Real-World and AI-Generated Images](https://mlsanity.com/paper/2405.03486/) (2024)
- [An Investigation of Large Language Models for Real-World Hate Speech Detection](https://mlsanity.com/paper/2401.03346/) (2024)
- [Adversarial Nibbler: An Open Red-Teaming Method for Identifying Diverse Harms in Text-to-Image Generation](https://mlsanity.com/paper/2403.12075/) (2024)
- [Moderating New Waves of Online Hate with Chain-of-Thought Reasoning in Large Language Models](https://mlsanity.com/paper/2312.15099/) (2023)
- [MIND: A Multi-agent Framework for Zero-shot Harmful Meme Detection](https://mlsanity.com/paper/2507.06908/) (2025)
- [Multi-Granular Multimodal Clue Fusion for Meme Understanding](https://mlsanity.com/paper/2503.12560/) (2025)
- [Toxic Memes: A Survey of Computational Perspectives on the Detection and Explanation of Meme Toxicities](https://mlsanity.com/paper/2406.07353/) (2024)
- [Evolver: Chain-of-Evolution Prompting to Boost Large Multimodal Models for Hateful Meme Detection](https://mlsanity.com/paper/2407.21004/) (2024)
- [More than Memes: A Multimodal Topic Modeling Approach to Conspiracy Theories on Telegram](https://mlsanity.com/paper/2410.08642/) (2024)
- [Hate in Plain Sight: On the Risks of Moderating AI-Generated Hateful Illusions](https://mlsanity.com/paper/2507.22617/) (2025)
- [Contextualizing Internet Memes Across Social Media Platforms](https://mlsanity.com/paper/2311.11157/) (2023)
- [When Memory Becomes a Vulnerability: Towards Multi-turn Jailbreak Attacks against Text-to-Image Generation Systems](https://mlsanity.com/paper/2504.20376/) (2025)
- [Bridging the Gap in Vision Language Models in Identifying Unsafe Concepts Across Modalities](https://mlsanity.com/paper/2507.11155/) (2025)
- [Multimodal Contrastive In-Context Learning](https://mlsanity.com/paper/2408.12959/) (2024)
- [M-QUEST -- Meme Question-Understanding Evaluation on Semantics and Toxicity](https://mlsanity.com/paper/2603.03315/) (2026)
- [Impact of Stickers on Multimodal Sentiment and Intent in Social Media: A New Task, Dataset and Baseline](https://mlsanity.com/paper/2405.08427/) (2024)
- [What Makes a Meme a Meme? Identifying Memes for Memetics-Aware Dataset Creation](https://mlsanity.com/paper/2407.11861/) (2024)
- [When Understanding Becomes a Risk: Authenticity and Safety Risks in the Emerging Image Generation Paradigm](https://mlsanity.com/paper/2603.24079/) (2026)
- [Prompt-enhanced Network for Hateful Meme Classification](https://mlsanity.com/paper/2411.07527/) (2024)
- [Meme Similarity and Emotion Detection using Multimodal Analysis](https://mlsanity.com/paper/2503.17493/) (2025)
- [Clustering Internet Memes Through Template Matching and Multi-Dimensional Similarity](https://mlsanity.com/paper/2505.00056/) (2025)
- [PIXELMOD: Improving Soft Moderation of Visual Misleading Information on Twitter](https://mlsanity.com/paper/2407.20987/) (2024)
- [What's Pulling the Strings? Evaluating Integrity and Attribution in AI Training and Inference through Concept Shift](https://mlsanity.com/paper/2504.21042/) (2025)
- [Beyond Hate: Differentiating Uncivil and Intolerant Speech in Multimodal Content Moderation](https://mlsanity.com/paper/2603.22985/) (2026)
- [SyRHM: Symbolic-Language-Enhanced Reasoning with Associative Retrieval for Zero-shot Harmful Meme Detection](https://mlsanity.com/paper/2609.13794/) (2026)

## Abstract (arXiv)

The dissemination of hateful memes online has adverse effects on social media platforms and the real world. Detecting hateful memes is challenging, one of the reasons being the evolutionary nature of memes; new hateful memes can emerge by fusing hateful connotations with other cultural ideas or symbols. In this paper, we propose a framework that leverages multimodal contrastive learning models, in particular OpenAI's CLIP, to identify targets of hateful content and systematically investigate the evolution of hateful memes. We find that semantic regularities exist in CLIP-generated embeddings that describe semantic relationships within the same modality (images) or across modalities (images and text). Leveraging this property, we study how hateful memes are created by combining visual elements from multiple images or fusing textual information with a hateful image. We demonstrate the capabilities of our framework for analyzing the evolution of hateful memes by focusing on antisemitic memes, particularly the Happy Merchant meme. Using our framework on a dataset extracted from 4chan, we find 3.3K variants of the Happy Merchant meme, with some linked to specific countries, persons, or organizations. We envision that our framework can be used to aid human moderators by flagging new variants of hateful memes so that moderators can manually verify them and mitigate the problem of hateful content online.