Culture Matters in Toxic Language Detection in Persian

TL;DR

This paper investigates how cultural context influences toxic language detection in Persian, comparing various machine learning methods and highlighting the importance of cultural similarity in transfer learning effectiveness.

Contribution

It introduces a comprehensive comparison of detection methods and emphasizes the role of cultural similarity in transfer learning for Persian toxic language detection.

Findings

Cultural similarity improves transfer learning performance

Fine-tuning and data enrichment enhance detection accuracy

Cross-lingual transfer varies with cultural proximity

Abstract

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.