Understanding how social norms vary across cultures can help us build culturally aligned NLP systems. We propose a culture agnostic approach to norm discovery, using moral emotions, shame and pride, to identify examples of normative expectations and extract corresponding social norms. We present the first cross cultural self-conscious emotions dataset, obtained from 5.4K Bollywood and Hollywood movies, along with over 10K extracted social norms. We validate our dataset using native speakers and demonstrate how our dataset reveals variations in social norms that align with the cultural dichotomy observed in these nations e.g., Bollywood movies emphasize shame due to deviation from social roles, and express pride in family honor, while Hollywood shames poverty and incompetence, and takes pride in ethical behavior. Notably, females are shamed more across both cultures and both cultures shame women for violating similar normative expectations.

翻译：理解社会规范如何随文化变化有助于构建与文化适配的自然语言处理系统。我们提出一种文化无关的规范发现方法，利用道德情绪——羞耻与自豪——识别规范性期望的实例并提取相应的社会规范。我们首次构建了跨文化自我意识情绪数据集，该数据集源自5400部宝莱坞与好莱坞电影，并包含超过1万条提取的社会规范。我们通过母语使用者验证数据集，并展示了数据集如何揭示与这些国家文化二分法相一致的社会规范差异，例如：宝莱坞电影强调因偏离社会角色而产生的羞耻，并表达对家族荣誉的自豪；而好莱坞则对贫困与无能感到羞耻，并以道德行为为荣。值得注意的是，在两种文化中女性均更易受到羞耻指责，且两种文化均对女性因违反类似规范性期望而施加羞耻。