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

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conference contribution
posted on 2023-11-29, 18:25 authored by Yiting QuYiting Qu, xinlei.he, Shannon Pierson, Michael BackesMichael Backes, Yang ZhangYang Zhang, Savvas Zannettou
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.

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Preferred Citation

Yiting Qu, Xinlei He, Shannon Pierson, Michael Backes, Yang Zhang and Savvas Zannettou. On the Evolution of (Hateful) Memes by Means of Multimodal Contrastive Learning. In: IEEE Symposium on Security and Privacy (S&P). 2023.

Primary Research Area

  • Trustworthy Information Processing

Name of Conference

IEEE Symposium on Security and Privacy (S&P)

Legacy Posted Date

2023-05-02

Open Access Type

  • Green

BibTeX

@inproceedings{cispa_all_3935, title = "On the Evolution of (Hateful) Memes by Means of Multimodal Contrastive Learning", author = "Qu, Yiting and He, Xinlei and Pierson, Shannon and Backes, Michael and Zhang, Yang and Zannettou, Savvas", booktitle="{IEEE Symposium on Security and Privacy (S&P)}", year="2023", }

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