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

Yiting Qu*, Xinlei He, Shannon Pierson, Michael Backes, Yang Zhang, Savvas Zannettou

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)


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.
Original languageEnglish
Title of host publicationProceedings - 44th IEEE Symposium on Security and Privacy, SP 2023
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages18
ISBN (Electronic)9781665493369
Publication statusPublished - 2023
Event44th IEEE Symposium on Security and Privacy, SP 2023 - Hybrid, San Francisco, United States
Duration: 22 May 202325 May 2023

Publication series

NameProceedings - IEEE Symposium on Security and Privacy
ISSN (Print)1081-6011


Conference44th IEEE Symposium on Security and Privacy, SP 2023
Country/TerritoryUnited States
CityHybrid, San Francisco

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Cite this