Using psychological theory to ground guidelines for the annotation of misogynistic language

• URL: http://arxiv.org/abs/2601.17417v1
• Date: Sat, 24 Jan 2026 11:29:46 GMT
• Title: Using psychological theory to ground guidelines for the annotation of misogynistic language
• Authors: Artemis Deligianni, Zachary Horne, Leonidas A. A. Doumas,
• Abstract summary: misogyny is on the rise both online and offline.<n>Current misogyny detection coding schemes and datasets fail to capture the ways women experience misogyny online.<n>We present a case study using Large Language Models (LLMs) to compare our coding scheme to a self-described "expert" misogyny annotation scheme in the literature.
• Score: 2.0391237204597368
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Detecting misogynistic hate speech is a difficult algorithmic task. The task is made more difficult when decision criteria for what constitutes misogynistic speech are ungrounded in established literatures in psychology and philosophy, both of which have described in great detail the forms explicit and subtle misogynistic attitudes can take. In particular, the literature on algorithmic detection of misogynistic speech often rely on guidelines that are insufficiently robust or inappropriately justified -- they often fail to include various misogynistic phenomena or misrepresent their importance when they do. As a result, current misogyny detection coding schemes and datasets fail to capture the ways women experience misogyny online. This is of pressing importance: misogyny is on the rise both online and offline. Thus, the scientific community needs to have a systematic, theory informed coding scheme of misogyny detection and a corresponding dataset to train and test models of misogyny detection. To this end, we developed (1) a misogyny annotation guideline scheme informed by theoretical and empirical psychological research, (2) annotated a new dataset achieving substantial inter-rater agreement (kappa = 0.68) and (3) present a case study using Large Language Models (LLMs) to compare our coding scheme to a self-described "expert" misogyny annotation scheme in the literature. Our findings indicate that our guideline scheme surpasses the other coding scheme in the classification of misogynistic texts across 3 datasets. Additionally, we find that LLMs struggle to replicate our human annotator labels, attributable in large part to how LLMs reflect mainstream views of misogyny. We discuss implications for the use of LLMs for the purposes of misogyny detection.

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