MemeGuard: An LLM and VLM-based Framework for Advancing Content Moderation via Meme Intervention

In the digital world, memes present a unique challenge for content moderation due to their potential to spread harmful content. Although detection methods have improved, proactive solutions such as intervention are still limited, with current research focusing mostly on textbased content, neglecting the widespread influence of multimodal content like memes. Addressing this gap, we present MemeGuard, a comprehensive framework leveraging Large Language Models (LLMs) and Visual Language Models (VLMs) for meme intervention. MemeGuard harnesses a specially fine-tuned VLM, VLMeme, for meme interpretation, and a multimodal knowledge selection and ranking mechanism (MKS) for distilling relevant knowledge. This knowledge is then employed by a general-purpose LLM to generate contextually appropriate interventions. Another key contribution of this work is the Intervening Cyberbullying in Multimodal Memes (ICMM) dataset, a high-quality, labeled dataset featuring toxic memes and their corresponding humanannotated interventions. We leverage ICMM to test MemeGuard, demonstrating its proficiency in generating relevant and effective responses to toxic memes. 1 Disclaimer: This paper contains harmful content that may be disturbing to some readers. * Work does not relate to position at Amazon. 1 Code and dataset are available at https://github.com/ Jhaprince/MemeGuard USER Ideal Intervention Making fun of someone's intelligence is hurtful and unnecessary. We should strive to treat others with kindness and empathy, rather than belittling them.