Psychological abuse, a form of intimate partner violence (IPV), involves non-physical behaviors meant to control, humiliate, punish, or threaten a partner. Despite its strong link to other forms of abuse, scholarly disagreement makes its definition ambiguous. Data-driven approaches, particularly machine learning (ML), remain underutilized in IPV research but offer advantages such as uncovering hidden patterns, efficiency, and scalability. This work applies natural language processing (NLP) modeling techniques (traditional modeling, fine-tuning, and few-shot learning) to classify six types of psychological abuse on a dataset of 1,500 labeled Reddit posts. Our LLaMA-3.70 few-shot model establishes a state-of-the-art baseline for this dataset. Not only does our study reveal that it is possible to employ NLP modeling to detect psychological abuse, it also demonstrates how some computational methods can make use of very limited datasets to produce high-quality results within the social sciences. We also apply an explainability measure (LIME) to surface model biases. This technique deepens understanding of model behavior, contextualizes performance, and sets a precedent for using computational methods in nuanced, human-centered social science research. This study demonstrates how ML paired with explainability measures can advance rigorous, responsible research in social disciplines involving ethical or semantic uncertainty.
Ashkenazi et al. (2026) studied this question.