The MISOMEM-Val Dataset for Identifying Human Values in Misogynistic Memes

Abstract

We present MISOMEM-Val, the first dataset that systematically annotates human values across Frames of Misogyny (FoMs) derived from misogynistic memes. Extending the Taxonomy of Misogyny, each frame is linked to the Human Value Hierarchy (HVH) with annotated support and ignore stances and accompanying rationales. In total, 1089 frames were annotated, comprising 3,051 support and 7,007 ignore value instances. We introduce Hierarchical Value Discovery with Human Feedback (HVD-HF), an LLM-assisted annotation framework combining Chain-of-Thought prompting and self-consistency verification to ensure transparency and quality. The annotation analysis reveals systematic asymmetries—Conservation and Self-Enhancement are frequently supported, while Self-Transcendence is often ignored, thus highlighting how misogynistic memes distort core human values.

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