M3Hop-CoT: Misogynous Meme Identification with Multimodal Multi-hop Chain-of-Thought

• URL: http://arxiv.org/abs/2410.09220v1
• Date: Fri, 11 Oct 2024 19:50:53 GMT
• Title: M3Hop-CoT: Misogynous Meme Identification with Multimodal Multi-hop Chain-of-Thought
• Authors: Gitanjali Kumari, Kirtan Jain, Asif Ekbal,
• Abstract summary: We introduce a Multimodal Multi-hop CoT (M3Hop-CoT) framework for Misogynous meme identification. M3Hop-CoT employs a three-step multimodal prompting principle to induce emotions, target awareness, and contextual knowledge for meme analysis. We evaluate the model's generalizability by evaluating it on various benchmark meme datasets.
• Score: 21.06134139986278
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, there has been a significant rise in the phenomenon of hate against women on social media platforms, particularly through the use of misogynous memes. These memes often target women with subtle and obscure cues, making their detection a challenging task for automated systems. Recently, Large Language Models (LLMs) have shown promising results in reasoning using Chain-of-Thought (CoT) prompting to generate the intermediate reasoning chains as the rationale to facilitate multimodal tasks, but often neglect cultural diversity and key aspects like emotion and contextual knowledge hidden in the visual modalities. To address this gap, we introduce a Multimodal Multi-hop CoT (M3Hop-CoT) framework for Misogynous meme identification, combining a CLIP-based classifier and a multimodal CoT module with entity-object-relationship integration. M3Hop-CoT employs a three-step multimodal prompting principle to induce emotions, target awareness, and contextual knowledge for meme analysis. Our empirical evaluation, including both qualitative and quantitative analysis, validates the efficacy of the M3Hop-CoT framework on the SemEval-2022 Task 5 (MAMI task) dataset, highlighting its strong performance in the macro-F1 score. Furthermore, we evaluate the model's generalizability by evaluating it on various benchmark meme datasets, offering a thorough insight into the effectiveness of our approach across different datasets.

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