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New framework MemeLogos enhances harmful meme detection with knowledge graphs

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced MemeLogos, a novel framework designed to improve the detection of harmful memes by incorporating structured knowledge and reasoning processes. This approach utilizes a knowledge graph to abstract and organize recurring harmfulness patterns and reasoning structures from various harmful examples. By combining structured knowledge retrieval with a multi-stage reasoning process, MemeLogos aims to enhance both the accuracy and interpretability of harmful meme detection, outperforming existing methods in extensive experiments. AI

IMPACT This research could lead to more reliable and interpretable tools for combating harmful content online.

RANK_REASON The cluster contains a research paper detailing a new framework for harmful meme detection. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework MemeLogos enhances harmful meme detection with knowledge graphs

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The cluster contains a research paper detailing a new framework for harmful meme detection. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Hexiang Gu, Saihui Hou, Changlan Liu, Min Ren, Huijia Wu, Hongxun Gu, Zhaofeng He ·

    MemeLogos: Knowledge-Driven Retrieval-Augmented Reasoning for Harmful Meme Detection

    arXiv:2610.15463v1 Announce Type: new Abstract: With the rapid proliferation of social media, harmful memes have become a growing threat to online ecosystems. Their harmfulness is often implicitly conveyed through complex interactions among visual, textual, and contextual cues, m…

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