MEMEWEAVER: Inter-Meme Graph Reasoning for Sexism and Misogyny Detection
This addresses online harassment targeting women by improving detection through graph-based methods, though it is incremental as it builds on existing multimodal approaches.
The paper tackled the problem of detecting sexism and misogyny in online content by developing MemeWeaver, a multimodal framework that uses inter-meme graph reasoning, and it outperformed state-of-the-art baselines on benchmarks like MAMI and EXIST with faster training convergence.
Women are twice as likely as men to face online harassment due to their gender. Despite recent advances in multimodal content moderation, most approaches still overlook the social dynamics behind this phenomenon, where perpetrators reinforce prejudices and group identity within like-minded communities. Graph-based methods offer a promising way to capture such interactions, yet existing solutions remain limited by heuristic graph construction, shallow modality fusion, and instance-level reasoning. In this work, we present MemeWeaver, an end-to-end trainable multimodal framework for detecting sexism and misogyny through a novel inter-meme graph reasoning mechanism. We systematically evaluate multiple visual--textual fusion strategies and show that our approach consistently outperforms state-of-the-art baselines on the MAMI and EXIST benchmarks, while achieving faster training convergence. Further analyses reveal that the learned graph structure captures semantically meaningful patterns, offering valuable insights into the relational nature of online hate.