Content moderation at scale faces the challenge of considering local cultural
distinctions when assessing content. While global policies aim to maintain
decision-making consistency and prevent arbitrary rule enforcement, they often
overlook regional variations in interpreting natural language as expressed in
content. In this study, we are looking into how moderation systems can tackle
this issue by adapting to local comprehension nuances. We train large language
models on extensive datasets of media news and articles to create culturally
attuned models. The latter aim to capture the nuances of communication across
geographies with the goal of recognizing cultural and societal variations in
what is considered offensive content. We further explore the capability of
these models to generate explanations for instances of content violation,
aiming to shed light on how policy guidelines are perceived when cultural and
societal contexts change. We find that training on extensive media datasets
successfully induced cultural awareness and resulted in improvements in
handling content violations on a regional basis. Additionally, these
advancements include the ability to provide explanations that align with the
specific local norms and nuances as evidenced by the annotators' preference in
our conducted study. This multifaceted success reinforces the critical role of
an adaptable content moderation approach in keeping pace with the ever-evolving
nature of the content it oversees.

大规模内容审核面临着在评估内容时考虑本地文化差异的挑战。该研究探讨了如何通过适应本地理解细微差别来解决这个问题，培训大型语言模型以捕捉跨地理区域的交流细节，并在关于何为冒犯内容的文化和社会变化时提供解释。研究发现，基于广泛的媒体数据集进行培训成功地培养了文化意识，并在区域基础上改善了处理内容违规行为的能力，表明灵活的内容审核方法在跟上内容不断演变的本质方面发挥了关键作用。