Culture Matters in Toxic Language Detection in Persian
This addresses the problem of safer online environments for Persian speakers, but it is incremental as it applies existing methods to a new language context.
The paper tackled toxic language detection in Persian, showing that cultural similarity between languages improves transfer learning results, with better performance from culturally similar languages compared to distinct ones.
Toxic language detection is crucial for creating safer online environments and limiting the spread of harmful content. While toxic language detection has been under-explored in Persian, the current work compares different methods for this task, including fine-tuning, data enrichment, zero-shot and few-shot learning, and cross-lingual transfer learning. What is especially compelling is the impact of cultural context on transfer learning for this task: We show that the language of a country with cultural similarities to Persian yields better results in transfer learning. Conversely, the improvement is lower when the language comes from a culturally distinct country. Warning: This paper contains examples of toxic language that may disturb some readers. These examples are included for the purpose of research on toxic detection.