Harmonizing Global Voices: Culturally-Aware Models for Enhanced Content
  Moderation
Alex J. Chan José Luis Redondo García Fabrizio Silvestri Colm O'Donnel Konstantina Palla
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.