Identifying social norms and their violation is a challenge facing several projects in computational science. This paper presents a novel approach to identifying social norm violations. We used GPT-3, zero-shot classification, and automatic rule discovery to develop simple predictive models grounded in psychological knowledge. Tested on two massive datasets, the models present significant predictive performance and show that even complex social situations can be functionally analyzed through modern computational tools.
Prof. Yair Neuman and the engineer Yochai Cohen at Ben-Gurion University of the Negev have designed an AI system that identifies social norm violations. They trained a system to identify ten social emotions: competence, politeness, trust, discipline, caring, agreeableness, success, conformity, decency, and loyalty.
The system, which was tested on two massive datasets of short texts, successfully characterized a written situation under one of these ten classifiers and could perceive if it was positive or negative. The researchers claim that their models present significant predictive performance and show that even complex social situations can be functionally analyzed through modern computational tools.
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